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 |
|---|---|---|---|---|
'''simple docstring'''
from __future__ import annotations
import pandas as pd
def lowerCamelCase__ ( SCREAMING_SNAKE_CASE : list[int] , SCREAMING_SNAKE_CASE : list[int] , SCREAMING_SNAKE_CASE : int ):
UpperCAmelCase = [0] * no_of_processes
UpperCAmelCase = ... | 447 |
'''simple docstring'''
import sys
import webbrowser
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
print('Googling.....')
_a : List[Any] = 'https://www.google.com/search?q=' + ' '.join(sys.argv[1:])
_a : Dict = re... | 447 | 1 |
'''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 timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from tra... | 160 |
'''simple docstring'''
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gp... | 160 | 1 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
'BridgeTower/bridgetower-base': 'https://huggingface.co/BridgeTower/bridgetower-base/blob/ma... | 291 |
from typing import List
import datasets
from datasets.tasks import AudioClassification
from ..folder_based_builder import folder_based_builder
lowercase_ = datasets.utils.logging.get_logger(__name__)
class __lowerCAmelCase ( folder_based_builder.FolderBasedBuilderConfig ):... | 291 | 1 |
'''simple docstring'''
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> list:
return [
txt[:a] + txt[a].upper() + txt[a + 1 :]
for a in range(len(UpperCAmelCase__ ) )
if txt[a].isalpha()
]
if __name__ == "__main__":
__import__('''doc... | 713 |
'''simple docstring'''
import os
import time
import pytest
from datasets.utils.filelock import FileLock, Timeout
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> int:
A_ = FileLock(str(tmpdir / """foo.lock""" ) )
A_ = FileLock(str(tmpdir / """foo.lock"... | 667 | 0 |
import unittest
import numpy as np
import torch
from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class __lowerCAmelCase ( unittest.TestCase ):
@prop... | 60 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
lowerCamelCase = {
"""configuration_trocr""": ["""TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TrOCRConfig... | 191 | 0 |
'''simple docstring'''
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_acceler... | 267 |
'''simple docstring'''
import random
from typing import Any
def UpperCAmelCase ( lowerCamelCase_ :list ):
'''simple docstring'''
for _ in range(len(lowerCamelCase_ ) ):
snake_case_ : Union[str, Any] = random.randint(0 , len(lowerCamelCase_ ) - 1 )
... | 267 | 1 |
import json
import re
from typing import TYPE_CHECKING, List, Optional, Tuple, Union
import numpy as np
from ...utils import is_tf_available, is_torch_available, logging
if TYPE_CHECKING:
if is_torch_available():
import torch
if is_tf_available():
import tensorflow as tf
... | 493 | """simple docstring"""
import math
def lowercase ( a__ : int ) -> str:
_UpperCamelCase = 0
_UpperCamelCase = 0
while num > 0:
_UpperCamelCase = num % 8
_UpperCamelCase = octal + (remainder * math.floor(math.pow(10 , a__ ) ))... | 420 | 0 |
'''simple docstring'''
from collections import deque
def __a(SCREAMING_SNAKE_CASE_ : Optional[Any] ):
'''simple docstring'''
_lowerCAmelCase = len(__a )
_lowerCAmelCase = deque()
_lowerCAmelCase = [False for _ in range(__a )]
_lowerCAmelCase ... | 720 |
'''simple docstring'''
from queue import Queue
from typing import TYPE_CHECKING, Optional
if TYPE_CHECKING:
from ..models.auto import AutoTokenizer
class lowerCAmelCase_ :
def _snake_case ( self , _lowerCAmelCase ) -> Tuple:
raise NotImplementedError()
def ... | 489 | 0 |
"""simple docstring"""
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational ... | 58 |
"""simple docstring"""
def snake_case ( _a: int , _a: int )-> int:
'''simple docstring'''
lowerCamelCase__ = 1 # To kept the Calculated Value
# Since C(n, k) = C(n, n-k)
if k > (n - k):
lowerCamelCase__ = n - k
# Calc... | 510 | 0 |
'''simple docstring'''
import numpy as np
class _SCREAMING_SNAKE_CASE :
def __init__( self : Optional[int] , __UpperCamelCase : List[str]=None , __UpperCamelCase : Any=None , __UpperCamelCase : Dict=None , __UpperCamelCase : List[Any]=None , __UpperCamelCase :... | 705 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor
_lowercase : List[str] =logging.get_logger(__name__)
class _SCREAMING_SNAKE_CASE (lowercase__ ):
def __init__( self : Any , *__UpperCamelCase : O... | 574 | 0 |
"""simple docstring"""
def UpperCamelCase ( _lowerCAmelCase : Optional[int] = 2000000 ) -> str:
_UpperCAmelCase : List[str] = [0 for i in range(n + 1 )]
_UpperCAmelCase : List[Any] = 1
_UpperCAmelCase : Optional[int... | 238 |
"""simple docstring"""
from __future__ import annotations
import math
from collections import Counter
from string import ascii_lowercase
def __SCREAMING_SNAKE_CASE ( A_ ):
lowerCAmelCase__ ,lowerCAmelCase__ : Dict = analyze_text(A_ )
lowerCAmelCase__ : Any ... | 450 | 0 |
"""simple docstring"""
def __lowercase ( lowerCamelCase_ : Union[str, Any] ):
'''simple docstring'''
if not isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
SCREAMING_SNAKE_CASE__ = F'''Input value of [number={number}] must be an integer'... | 700 |
"""simple docstring"""
import os
from bleurt import score # From: git+https://github.com/google-research/bleurt.git
import datasets
_lowerCamelCase = datasets.logging.get_logger(__name__)
_lowerCamelCase = '\\n@inproceedings{bleurt,\n title={BLEURT: Learning Robust Metrics for Text Generation... | 112 | 0 |
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 ...... | 686 |
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 impor... | 686 | 1 |
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_flax_common import Fla... | 716 |
import argparse
import os
import re
lowerCamelCase__ = '''src/transformers'''
# Pattern that looks at the indentation in a line.
lowerCamelCase__ = re.compile(R'''^(\s*)\S''')
# Pattern that matches `"key":" and puts `key` in group 0.
lowerCamelCase__ = re.compile(R'''^\s*"(... | 226 | 0 |
'''simple docstring'''
def __snake_case ( UpperCAmelCase_ : int = 1000000 ):
lowerCamelCase_ = [i - 1 for i in range(limit + 1 )]
for i in range(2 , limit + 1 ):
if phi[i] == i - 1:
for j in range(2 * i , limit + 1 , UpperCAmelCase_ ):
p... | 675 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
a_ : Optional[Any] = {
"""configuration_ctrl""": ["""CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CTRLConfig"""],
"""... | 675 | 1 |
'''simple docstring'''
from math import sqrt
def _lowerCamelCase ( A_ : Any ) -> str:
'''simple docstring'''
UpperCamelCase__ : List[Any] =0
for i in range(1 , int(sqrt(SCREAMING_SNAKE_CASE_ ) + 1 ) ):
if n % i == 0 and i != sqrt(SCREAMING_SNAKE_CASE_ )... | 715 |
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 import (
is_pipeline_test,
nes... | 582 | 0 |
import argparse
from pathlib import Path
from typing import Dict, OrderedDict, Tuple
import torch
from audiocraft.models import MusicGen
from transformers import (
AutoFeatureExtractor,
AutoTokenizer,
EncodecModel,
MusicgenDecoderConfig,
MusicgenForConditionalGeneration,
MusicgenProcessor,
... | 439 |
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 floa... | 439 | 1 |
from datetime import datetime
import requests
def lowercase_ ( _A : str ):
"""simple docstring"""
lowerCamelCase__ : Any = "https://downloadgram.net/wp-json/wppress/video-downloader/video?url="
lowerCamelCase__ : Tuple = requests.get(ba... | 5 |
import os
from pathlib import Path
def lowercase_ ( ):
"""simple docstring"""
from torch.utils.cpp_extension import load
lowerCamelCase__ : Any = Path(_A ).resolve().parent.parent.parent / "kernels" / "deformable_detr"
lowerCamelCase__ : Optiona... | 5 | 1 |
'''simple docstring'''
import os
import random
import sys
from . import cryptomath_module as cryptoMath # noqa: N812
from . import rabin_miller as rabinMiller # noqa: N812
def UpperCamelCase__ ( ):
"""simple docstring"""
print("""Making key files...""... | 207 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_torch_available,
)
A__ : Any ={
'''configuration_speecht5''': [
'''SPEECHT5_PRETRAINED_... | 207 | 1 |
def lowerCamelCase_ ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
SCREAMING_SNAKE_CASE = set()
# To detect a back edge, keep track of vertices currently in the recursion stack
SCREAMING_SNAKE_CASE = set()
return any(
node not in visited and depth_fi... | 450 |
import math
from typing import Optional
import numpy as np
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : List[Any] = logging.get_logger(__name__)
__A : Tuple = {
"""facebook/encodec_24khz""": """https://huggingface.co/facebook/en... | 450 | 1 |
"""simple docstring"""
from ..utils import is_flax_available, is_torch_available
if is_torch_available():
from .autoencoder_kl import AutoencoderKL
from .controlnet import ControlNetModel
from .dual_transformer_ad import DualTransformeraDModel
from .modeling_utils import ModelMixin
from .prior_tr... | 93 |
'''simple docstring'''
import argparse
import datetime
def lowerCamelCase__ ( A : str ):
'''simple docstring'''
UpperCAmelCase = {
'''0''': '''Sunday''',
'''1''': '''Monday''',
'''2''': '''Tuesday''',
'''3''': '''Wednesday''',
... | 210 | 0 |
_snake_case = {
"""a""": """AAAAA""",
"""b""": """AAAAB""",
"""c""": """AAABA""",
"""d""": """AAABB""",
"""e""": """AABAA""",
"""f""": """AABAB""",
"""g""": """AABBA""",
"""h""": """AABBB""",
"""i""": """ABAAA""",
"""j""": """BBBAA""",
"""k""": """ABAAB""",
... | 611 |
import argparse
import numpy as np
import torch
from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging
logging.set_verbosity_info()
_snake_case = logging.get_logger("""transformers.models.speecht5""")
def _A ( __magic_name__ , __magic_name__ , __magic_n... | 611 | 1 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, Features, Value
from .base import TaskTemplate
@dataclass(frozen=lowerCamelCase__)
class lowerCamelCase__ ( lowerCamelCase__):
'''simple docstring'''
snake_case_ =field(... | 647 |
"""simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_a = logging.get_logger(__name__)
_a = {
"""microsoft/wavlm-base""": """https://huggingface.co/microsoft/wavlm-base/resolve/main... | 19 | 0 |
'''simple docstring'''
from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available
if is_datasets_available():
import datasets
... | 318 |
'''simple docstring'''
_lowerCAmelCase = "Input must be a string of 8 numbers plus letter"
_lowerCAmelCase = "TRWAGMYFPDXBNJZSQVHLCKE"
def _lowerCAmelCase ( lowercase : str ) ->bool:
"""simple docstring"""
if not isinstance(lowercase... | 318 | 1 |
"""simple docstring"""
from __future__ import annotations
import unittest
import numpy as np
from transformers import LayoutLMConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common i... | 498 |
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_float32 (there's also the fix_lavis branch)
# also note: to convert Vicuna checkpoints, we had to include /home/niel... | 37 | 0 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import 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_tens... | 508 |
'''simple docstring'''
import socket
def lowercase__( ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = socket.socket(socket.AF_INET ,socket.SOCK_STREAM )
SCREAMING_SNAKE_CASE : Any = socket.gethostname()
... | 508 | 1 |
'''simple docstring'''
def A__ ( __lowerCAmelCase : List[str] , __lowerCAmelCase : List[Any] ):
# "extended trapezoidal rule"
# int(f) = dx/2 * (f1 + 2f2 + ... + fn)
lowerCamelCase__ = (boundary[1] - boundary[0]) / steps
lowerCamelCase__ ... | 50 |
"""simple docstring"""
def _A (__a ) -> bool:
"""simple docstring"""
return str(__a ) == str(__a )[::-1]
def _A (__a ) -> int:
"""simple docstring"""
return int(__a ) + int(str(__a )[::-1] )
def _A (__a = 1_00_00 ) ... | 512 | 0 |
import math
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput
class UpperCamelCase_ ( snake_case_ , snake_case_ ):
'''simple ... | 713 |
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import Con... | 607 | 0 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from diffusers import DDIMScheduler, KandinskyVaaPipeline, KandinskyVaaPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffuser... | 111 |
'''simple docstring'''
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
... | 111 | 1 |
from __future__ import annotations
import os
from collections.abc import Mapping
_lowercase = tuple[int, int]
class _UpperCAmelCase :
def __init__( self , a__ , a__):
A__ = vertices
A__ = {
(min(a__), max(a__)): weight for edge, wei... | 712 |
import inspect
import unittest
from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import Conf... | 526 | 0 |
from itertools import product
from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros
def _UpperCAmelCase ( UpperCAmelCase : Any , UpperCAmelCase : Dict ):
"""simple docstring"""
... | 519 |
from math import atan, cos, radians, sin, tan
from .haversine_distance import haversine_distance
__UpperCamelCase : Optional[Any] = 6_378_137.0
__UpperCamelCase : Any = 6_356_752.314_245
__UpperCamelCase : Optional[int] = 6378137
def _UpperCAmelCase ( Up... | 519 | 1 |
from typing import Optional, Union
import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention
from ...modeling_uti... | 712 |
def lowercase_ ( SCREAMING_SNAKE_CASE : list[int] , SCREAMING_SNAKE_CASE : list[int] ):
"""simple docstring"""
# Check if the input is valid
if not len(SCREAMING_SNAKE_CASE ) == len(SCREAMING_SNAKE_CASE ) == 3:
raise ValueError('''Please enter a valid equation.''' )
if... | 408 | 0 |
def lowercase ( __A : int ) -> str:
'''simple docstring'''
if isinstance(__A , __A ):
raise TypeError("""'float' object cannot be interpreted as an integer""" )
if isinstance(__A , __A ):
raise TypeError("""'str' object cannot be interpreted as an inte... | 36 |
import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig
if TYPE_CHECKING:
from ... import Pr... | 36 | 1 |
'''simple docstring'''
# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from diffusers.configur... | 0 |
'''simple docstring'''
import numpy as np
import torch
from torch.utils.data import Dataset
from utils import logger
class _a (_lowerCamelCase):
"""simple docstring"""
def __init__( self , A__ , A__ ) -> Any:
_SCREAMING_SNAKE_CASE ... | 0 | 1 |
class snake_case__ :
'''simple docstring'''
def __init__( self : List[Any] ) -> Any:
UpperCAmelCase_ = 0
UpperCAmelCase_ = 0
UpperCAmelCase_ = {}
def UpperCamelCase ( ... | 121 |
def _a ( SCREAMING_SNAKE_CASE__ : List[Any]=2_81_23 ) -> Any:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[Any] = [1] * (limit + 1)
for i in range(2 , int(limit**0.5 ) + 1 ):
sum_divs[i * i] += i
... | 663 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase : Optional[Any] = logging.get_logger(__name__)
_lowerCamelCase : Optional[int] = {
"""tiiuae/falcon-40b""": """https://huggingface.co/tiiuae/falco... | 715 | '''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase : List[Any] = logging.get_logger(__name__)
_lowerCamelCase : Any = {
"""facebook/s2t-wav2vec2-large-en-de""": (
"""https://huggi... | 512 | 0 |
"""simple docstring"""
def __snake_case ( _lowercase ):
"""simple docstring"""
UpperCamelCase = []
UpperCamelCase = []
UpperCamelCase = {
'''^''': 3,
'''*''': 2,
'''/''': 2,
'''%''': 2,
'''+'''... | 34 |
'''simple docstring'''
from __future__ import annotations
from fractions import Fraction
def lowerCamelCase__ ( __lowerCamelCase : int , __lowerCamelCase : int ):
'''simple docstring'''
return (
num != den and num % 1_0 == den // 1_0 and (num // 1_0) / (d... | 446 | 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 _UpperCAmelC... | 703 |
"""simple docstring"""
import sys
def UpperCAmelCase__ ( _UpperCAmelCase ):
"""simple docstring"""
A_ : Dict = len(_UpperCAmelCase )
A_ : int = [[0 for x in range(_UpperCAmelCase )] for x in range(_UpperCAmelCase )]
A_ : T... | 302 | 0 |
"""simple docstring"""
import string
from math import logaa
def lowerCamelCase_( _lowerCamelCase , _lowerCamelCase ) -> int:
'''simple docstring'''
_lowerCamelCase : Tuple = document.translate(
str.maketrans("" , "" , string.punctuation ... | 46 |
'''simple docstring'''
import importlib.metadata
import operator
import re
import sys
from typing import Optional
from packaging import version
__lowerCamelCase : List[str] = {
'<': operator.lt,
'<=': operator.le,
'==': operator.eq,
'!=': operator.ne,
'>=': operator.ge,
'>':... | 404 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
SCREAMING_SNAKE_CASE = {
"""configuration_roc_bert""": ["""ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RoCBertConfig"""],
... | 556 |
"""simple docstring"""
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
from PIL import Image
from ...utils import (
BaseOutput,
OptionalDependencyNotAvailable,
is_flax_available,
is_k_diffusion_available,
is_k_diffusion_ve... | 556 | 1 |
import string
def lowerCamelCase ( UpperCamelCase : str ) -> None:
for key in range(len(string.ascii_uppercase ) ):
_lowerCamelCase = ''
for symbol in message:
if symbol in string.ascii_uppercase:
_lowerCamelCase = string.ascii_uppercase... | 544 | from __future__ import annotations
from bisect import bisect_left
from functools import total_ordering
from heapq import merge
@total_ordering
class lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
def __lt__( self : List[Any] , sn... | 544 | 1 |
"""simple docstring"""
import os
import torch
from ..logging import get_logger
from .constants import FSDP_PYTORCH_VERSION, MODEL_NAME, OPTIMIZER_NAME
from .versions import is_torch_version
if is_torch_version(""">=""", FSDP_PYTORCH_VERSION):
import torch.distributed.checkpoint as dist_c... | 16 |
"""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 __Upper... | 16 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import XLMRobertaTokenizer
from diffusers import (
AltDiffusionImgaImgPipeline,
AutoencoderKL,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
fro... | 14 |
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
AutoConfig,
AutoImageProcessor,
CLIPConfig,
CLIPImageProcessor,
)
from transformers.testing_utils import DUM... | 14 | 1 |
"""simple docstring"""
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.mo... | 714 |
"""simple docstring"""
# flake8: noqa
# Lint as: python3
from typing import Dict, List, Optional, Type
from .. import config
from ..utils import logging
from .formatting import (
ArrowFormatter,
CustomFormatter,
Formatter,
PandasFormatter,
PythonFormatter,
T... | 492 | 0 |
'''simple docstring'''
from __future__ import annotations
import time
import numpy as np
lowerCamelCase = [8, 5, 9, 7]
lowerCamelCase = [
[2, 0, 1, 1],
[0, 1, 2, 1],
[4, 0, 0, 3],
[0, 2, 1, 0],
[1, 0, 3, 0],
]
lowerCamelCase = [
[3, 2, 1, 4],
... | 474 |
'''simple docstring'''
from __future__ import annotations
from typing import Any
def _A ( _lowerCAmelCase ):
"""simple docstring"""
create_state_space_tree(_lowerCAmelCase , [] , 0 )
def _A ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ):
... | 474 | 1 |
import json
import os
import shutil
import tempfile
import unittest
from multiprocessing import get_context
from pathlib import Path
import datasets
import numpy as np
from datasets import load_dataset
from parameterized import parameterized
from transformers import AutoProcessor
from tran... | 332 |
import argparse
import intel_extension_for_pytorch as ipex
import torch
from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline
_UpperCamelCase : str =argparse.ArgumentParser('Stable Diffusion script with intel optimization', add_help=False)
parser.add_argument('--dpm', ... | 332 | 1 |
def lowerCAmelCase__(__snake_case ,__snake_case ) -> bool:
'''simple docstring'''
lowerCamelCase__ = len(a_ )
lowerCamelCase__ = [[False] * (required_sum + 1) for _ in range(arr_len + 1 )]
# for each arr value, a sum of zero(0) can be formed ... | 481 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase__ : Tuple = logging.get_logger(__name__)
lowerCamelCase__ : Optional[Any] = {
"microsoft/markuplm-base": "https://huggingface.co/microsoft/markuplm-bas... | 698 | 0 |
"""simple docstring"""
import heapq
import sys
import numpy as np
SCREAMING_SNAKE_CASE__:Optional[int] = tuple[int, int]
class snake_case__ :
def __init__( self ):
__a = []
__a = set()
def a__ ( self ):
if not self.empty():
retu... | 704 | """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 | 0 |
"""simple docstring"""
import webbrowser
from sys import argv
from urllib.parse import parse_qs, quote
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
a_ = """%20""".join(argv[1:]) if len(argv) > 1 else quote(str(input("""Search: """))... | 437 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor
a_ = logging.get_logger(__name__)
class A_(SCREAMING_SNAKE_CASE_ ):
"""simple docstring"""
def __init__( self , *A , **A ... | 437 | 1 |
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
_SCREAMING_SNAKE_CASE : Tuple = '\\n@misc{chen2021evaluating,\n title={Evaluating... | 714 |
def __lowerCAmelCase ( __magic_name__ ):
return sum(i for i in range(1 , number // 2 + 1 ) if number % i == 0 ) == number
if __name__ == "__main__":
print('Program to check whether a number is a Perfect number or not...')
_SCREAMING_SNAKE_CASE : str = int(input(... | 206 | 0 |
"""simple docstring"""
import numpy as np
import pandas as pd
from sklearn.preprocessing import Normalizer
from sklearn.svm import SVR
from statsmodels.tsa.statespace.sarimax import SARIMAX
def __UpperCAmelCase ( lowercase ,lowercase ,lowercase ,lowercase ,lowercase ):
"""simple docstr... | 277 | '''simple docstring'''
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,
AutoModelFo... | 614 | 0 |
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 (
BertTokenizer,
ViltConfig,
ViltForImageAndTextRetrieval,
ViltForImagesAndTextClassification,
ViltForMaskedLM,
ViltForQ... | 702 |
from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def A_ ( snake_case : int ) -> int:
'''simple docstring'''
def is_in_circle(snake_case : float , snake_case : float ) -> bool:
... | 451 | 0 |
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.testing_ut... | 101 |
"""simple docstring"""
import shutil
import tempfile
import unittest
from unittest.mock import patch
from transformers import (
DefaultFlowCallback,
IntervalStrategy,
PrinterCallback,
ProgressCallback,
Trainer,
TrainerCallback,
TrainingArguments,
is_torch_ava... | 545 | 0 |
import argparse
import torch
from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert
logging.set_verbosity_info()
def snake_case__ ( __lowercase , __lowercase , __lowercase ) -> int:
"""simple docstring"""
... | 719 |
import logging
import random
import ray
from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex
snake_case : Any = logging.getLogger(__name__)
class lowerCAmelCase__ :
def __init__( self : ... | 182 | 0 |
"""simple docstring"""
from dataclasses import dataclass
from typing import Optional
import torch
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .modeling_utils import ModelMixin
@datac... | 273 |
"""simple docstring"""
from collections import deque
from math import floor
from random import random
from time import time
class A__ :
"""simple docstring"""
def __init__( self: Union[str, Any] )-> List[str]:
lowerCamelCase : Optional[int] = ... | 222 | 0 |
'''simple docstring'''
import numpy as np
from scipy.spatial.distance import cdist
from sklearn.metrics import fa_score
import datasets
UpperCamelCase__ ='\\n @inproceedings{kakwani2020indicnlpsuite,\n title={{IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingua... | 719 |
import argparse
import torch
from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def lowerCamelCase__ (__lowerCamelCase, __lowerCamelCase, __lowerCamelCase ):
# Const... | 381 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import _LazyModule
UpperCamelCase__ = {'tokenization_bertweet': ['BertweetTokenizer']}
if TYPE_CHECKING:
from .tokenization_bertweet import BertweetTokenizer
else:
import sys
UpperCamelCase__ = _LazyModule(__n... | 110 |
from __future__ import annotations
_lowerCAmelCase : Optional[Any] = {
'A': ['B', 'C', 'E'],
'B': ['A', 'D', 'E'],
'C': ['A', 'F', 'G'],
'D': ['B'],
'E': ['A', 'B', 'D'],
'F': ['C'],
'G': ['C'],
}
class lowerCAmelCase :
'''simple docs... | 246 | 0 |
'''simple docstring'''
import argparse
import collections
import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def snak... | 718 |
'''simple docstring'''
import copy
import os
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
from datasets.arrow_writer import ArrowWriter, OptimizedTypedSequence, ParquetWriter, ... | 377 | 0 |
'''simple docstring'''
import os
from pathlib import Path
from unittest.mock import patch
import pytest
import zstandard as zstd
from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
OfflineModeIsEnabled,
cached_path,
fsspec_get,
fsspec_head,
ft... | 588 |
'''simple docstring'''
from heapq import heappop, heappush
import numpy as np
def SCREAMING_SNAKE_CASE ( lowercase_ : np.ndarray , lowercase_ : tuple[int, int] , lowercase_ : tuple[int, int] , lowercase_ : bool , ):
lowercase , lowercase = grid.shape
... | 588 | 1 |
'''simple docstring'''
from __future__ import annotations
def lowercase__ ( __UpperCamelCase )-> list[int]:
if len(__UpperCamelCase ) == 0:
return array
UpperCamelCase ,UpperCamelCase = min(__UpperCamelCase ), ma... | 35 |
'''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 keep... | 35 | 1 |
import sys
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... | 47 | from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__magic_name__ ={
'''configuration_roformer''': ['''ROFORMER_PRETRAINED_CONFIG_ARCHIVE_... | 415 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
lowercase_ = {
'''configuration_clip''': [
... | 700 |
# 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... | 336 | 0 |
"""simple docstring"""
class A_ :
def __init__( self: Optional[int] ):
'''simple docstring'''
_lowerCamelCase : Dict = {}
def _lowercase ( self: str ):
'''simple docstring'''
print(self.vertex )... | 46 |
from math import factorial
class __lowercase :
"""simple docstring"""
def __init__( self , __UpperCAmelCase , __UpperCAmelCase ) -> Optional[int]:
A : Union[str, Any] = real
if isinstan... | 542 | 0 |
"""simple docstring"""
import inspect
import tempfile
from collections import OrderedDict, UserDict
from collections.abc import MutableMapping
from contextlib import ExitStack, contextmanager
from dataclasses import fields
from enum import Enum
from typing import Any, ContextManager, List, Tuple
import nu... | 176 |
"""simple docstring"""
import logging
import os
from typing import Dict, List, Optional, Union
import torch
import torch.nn as nn
from accelerate.utils.imports import (
is_abit_bnb_available,
is_abit_bnb_available,
is_bnb_available,
)
from ..big_modeling import dispatch_model, init_empty_wei... | 176 | 1 |
# Author: OMKAR PATHAK, Nwachukwu Chidiebere
# Use a Python dictionary to construct the graph.
from __future__ import annotations
from pprint import pformat
from typing import Generic, TypeVar
_lowercase : Union[str, Any] =TypeVar("T")
class snake_case__ (Generic[T] ):
... | 136 |
from importlib import import_module
from .logging import get_logger
_lowercase : Optional[int] =get_logger(__name__)
class snake_case__ :
"""simple docstring"""
def __init__( self , __lowercase , __lowercase=None ) -> Dict:
... | 136 | 1 |
import argparse
import os
import re
import packaging.version
lowerCAmelCase : Any = 'examples/'
lowerCAmelCase : Optional[int] = {
'examples': (re.compile(R'^check_min_version\("[^"]+"\)\s*$', re.MULTILINE), 'check_min_version("VERSION")\n'),
'init': (re.compile(R'^__versi... | 353 |
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 ..auto import CONFIG_MAPPING
lowerCAmelCase : Dict = logging.get_logger(__name__)
lowe... | 353 | 1 |
"""simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConfig,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaForCTC,
WavaVecaForPreTraining,
Wav... | 103 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCamelCase = logging.get_logger(__name__)
__lowerCamelCase = {
'''google/pix2struct-textcaps-base''': (
'''https:/... | 288 | 0 |
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import TimesformerConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import... | 583 |
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 ...modeling_outputs import (
BackboneOutput,
BaseModelOutputWithNoAttention,
BaseModelOutputWithPoo... | 583 | 1 |
import inspect
import re
from hashlib import shaaaa
from typing import Dict, List
from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql # noqa F40... | 629 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
__lowerCamelCase : str = {"""tokenization_byt5""": ["""ByT5Tokenizer"""]}
if TYPE_CHECKING:
from .tokenization_byta import ByTaTokenizer
else:
import sys
__lowerCamelCase : List[str] = _LazyModule(__name__, glo... | 629 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__ = logging.get_logger(__name__)
UpperCamelCase__ = {
'facebook/xglm-564M': 'https://huggingface.co/facebook/xglm-564M/resolve/main/config.json',
# See all XGLM models ... | 707 |
"""simple docstring"""
from __future__ import annotations
from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass
@dataclass
class a :
UpperCamelCase : int
UpperCamelCase : int
class a :
def __init__( ... | 254 | 0 |
# 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 app... | 55 |
"""simple docstring"""
from __future__ import annotations
def lowercase (_lowerCAmelCase ):
__lowerCAmelCase = 2
__lowerCAmelCase = []
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factor... | 465 | 0 |
A = [
[0, 16, 13, 0, 0, 0],
[0, 0, 10, 12, 0, 0],
[0, 4, 0, 0, 14, 0],
[0, 0, 9, 0, 0, 20],
[0, 0, 0, 7, 0, 4],
[0, 0, 0, 0, 0, 0],
]
def _lowerCamelCase( lowerCAmelCase__ : Optional[int] , lowerCAmelCase__ : List[str] , lowerCAmelC... | 706 |
def _lowerCamelCase( ):
'''simple docstring'''
return 1
def _lowerCamelCase( lowerCAmelCase__ : int ):
'''simple docstring'''
return 0 if x < 0 else two_pence(x - 2 ) + one_pence()
def _lowerCamelCase( lowerCAmelCase__ : int ... | 97 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
__a :List[Any] = {'configuration_speech_encoder_decoder': ['SpeechEncoderDecoderConfig']}
try:
if not is_torch_available():
raise Optional... | 86 |
def lowerCamelCase__ ( a : list , a : list , a : int , a : int , a : int ) -> int:
"""simple docstring"""
if index == number_of_items:
return 0
a__ :str = 0
a__ :Union[str, Any] = 0
a__ :Optional[int... | 395 | 0 |
'''simple docstring'''
import os
from pickle import UnpicklingError
from typing import Dict, Tuple
import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict, unflatten_dict
import transformers
from .utils import logging
_U... | 575 |
'''simple docstring'''
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCamelCase : Dict =logging.get_logger(__name__)
_UpperCamelCase : List[str] ={
"huggingface/informer-tourism-monthly": (
... | 575 | 1 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.activations import gelu_new, gelu_python, get_activation
@require_torch
class __UpperCamelCase ( unittest.TestCase... | 80 |
'''simple docstring'''
import os
from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE, hf_cache_home
__A : List[str] = HUGGINGFACE_HUB_CACHE
__A : Optional[int] = 'config.json'
__A : List[str] = 'diffusion_pytorch_model.bin'
__A : Tuple ... | 394 | 0 |
import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset
from ..utils.generic import ModelOutput
class lowerCamelCase ( lowercase__ ):
'''simple docstring'''
def __init__( self , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase ):
... | 23 |
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
SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
SCREAMING... | 23 | 1 |
class __lowercase :
def __init__( self , a__ , a__ , a__ ) -> Dict:
'''simple docstring'''
A_ = None
A_ = None
A_ = graph
self._normalize_graph(a__ ... | 141 |
from collections import OrderedDict
from typing import List, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
SCREAMING_SNAKE_CASE : List[str] = logging.get_logger(__name__)
SCREAMING_... | 141 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
UpperCAmelCase = {
"""configuration_mobilenet_v2""": [
"""MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""MobileNetV2Con... | 713 | """simple docstring"""
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 ... | 342 | 0 |
import unittest
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
if is_torch_available():
import torch
from transformers import AutoModelForImageClassification
if is_vision_available():
from transformers... | 348 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE ( __UpperCamelCase , __UpperCamelCase) -> set[str]:
a , a = set(__UpperCamelCase), [start]
while stack:
a = stack.pop()
explored.add(__UpperCamelCase)
# Differences from BF... | 515 | 0 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_electra import ElectraTokenizer
_A = {'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}
_A ... | 325 |
import argparse
import requests
import torch
from PIL import Image
from torchvision.transforms import Compose, Normalize, Resize, ToTensor
from transformers import SwinaSRConfig, SwinaSRForImageSuperResolution, SwinaSRImageProcessor
def __UpperCamelCase ( _A ):
lowerCAmelCase_ = Swi... | 325 | 1 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_tor... | 532 |
"""simple docstring"""
import unittest
import numpy as np
import torch
from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class lowercase ( uni... | 532 | 1 |
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_identified_filename,
infer_sh... | 712 |
'''simple docstring'''
import jax.numpy as jnp
from ...utils import logging
from ..ta.modeling_flax_ta import FlaxTaEncoderModel, FlaxTaForConditionalGeneration, FlaxTaModel
from .configuration_mta import MTaConfig
__a = logging.get_logger(__name__)
__a = "T5Config"
def __snake_case( ... | 301 | 0 |
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 im... | 79 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
ImageInput,
PILImageResampli... | 671 | 0 |
from __future__ import annotations
def A_ ( A__ ) -> bool:
a__ : List[Any] = len(A__ )
# We need to create solution object to save path.
a__ : Dict = [[0 for _ in range(A__ )] for _ in range(A__ )]
a__ : Tuple = run_... | 702 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase : List[str] = {
"""configuration_autoformer""": [
"""AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""AutoformerCon... | 392 | 0 |
from __future__ import annotations
def A ( lowercase__ : int ) -> list[int]:
UpperCamelCase__ :Tuple = 2
UpperCamelCase__ :Dict = []
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.append(lowercase__ )
if n > 1:
factors.append(lowercase__ )
retu... | 45 |
'''simple docstring'''
import unittest
from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
@require_sente... | 72 | 0 |
from __future__ import annotations
import time
from collections.abc import Sequence
from random import randint
from matplotlib import pyplot as plt
def a ( _UpperCAmelCase : Sequence[float] , _UpperCAmelCase : int , _UpperCAmelCase : int ):
... | 720 |
import subprocess
import sys
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
from transformers.testing_utils import TestCasePlus, require_torch
class UpperCAmelCase__ ( __UpperCamelCase ):
'''simple docstring'''
@require_torch
def sn... | 241 | 0 |
import unittest
import numpy as np
from transformers.testing_utils import is_flaky, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
... | 40 |
'''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class UpperCAmelCase ( UpperCamelCase__ ):
__lowercase = ["""image_processor""", """tokenizer"""]
__lowercase = """CLIPImage... | 440 | 0 |
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union
# Build the list of all feature extractors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from... | 715 |
from typing import Any
def a (_lowerCAmelCase ):
if not input_list:
return []
SCREAMING_SNAKE_CASE_ = [input_list.count(_lowerCAmelCase ) for value in input_list]
SCREAMING_SNAKE_CASE_ = max(_lowerCAmelCase ) # Gets the maximum count in t... | 89 | 0 |
import os
import pytest
from attr import dataclass
UpperCAmelCase_ = """us-east-1""" # defaults region
@dataclass
class lowerCamelCase__ :
"""simple docstring"""
a__ : str
a__ : List[str] = "arn:aws:iam::558105141721:role/sagemaker_execution_role"
a__ : List[A... | 2 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = {"""openai-gpt""": """https://huggingface.co/openai-gpt/resolve/main/config.json"""}
class lowerCamelCase__ ( _A):
"""simple ... | 2 | 1 |
def _a ( SCREAMING_SNAKE_CASE__ : List[str]=2_81_23 ) -> List[str]:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[Any] = [1] * (limit + 1)
for i in range(2 , int(limit**0.5 ) + 1 ):
s... | 157 |
import math
def _a ( SCREAMING_SNAKE_CASE__ : int ) -> bool:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : int = math.loga(math.sqrt(4 * positive_integer + 1 ) / 2 + 1 / 2 )
return exponent == int(SCREAM... | 157 | 1 |
import math
from collections.abc import Iterator
from itertools import takewhile
def lowerCamelCase_ ( lowerCAmelCase__ : Dict ) -> bool:
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or n... | 106 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase : str = logging.get_logger(__name__)
UpperCAmelCase : Optional[int] = {
"edbeeching/decision-transformer-gym-hopper-medium": (
"https://huggingface... | 567 | 0 |
from typing import Callable, Dict, Optional, Tuple
import torch
from torch import nn
from torch.distributions import (
AffineTransform,
Distribution,
Independent,
NegativeBinomial,
Normal,
StudentT,
TransformedDistribution,
)
class A_ ( __lowerCamelCase ):
'''simple... | 708 |
import json
import os
import shutil
import warnings
from argparse import ArgumentParser, Namespace
from pathlib import Path
from typing import List
from ..utils import logging
from . import BaseTransformersCLICommand
try:
from cookiecutter.main import cookiecutter
UpperCAmelCase = True
except ImportError... | 565 | 0 |
'''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
__SCREAMING_SNAKE_CASE : Optional[Any] = logging.get_logger(__name__)
__SCREAM... | 244 | '''simple docstring'''
import unittest
from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
if is_flax_... | 244 | 1 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class lowerCamelCase_ ( metaclass=lowercase ):
"""simple docstring"""
_lowerCAmelCase : List[Any] = ["sentencepiece"]
def __init__( self , *UpperCAmelCase__ ... | 112 |
"""simple docstring"""
import numpy as np
import torch
from imwatermark import WatermarkEncoder
# Copied from https://github.com/Stability-AI/generative-models/blob/613af104c6b85184091d42d374fef420eddb356d/scripts/demo/streamlit_helpers.py#L66
_lowerCamelCase = 0b1011_0011_1110_1100_1001_0000_0111_1011_... | 112 | 1 |
class __lowercase : # Public class to implement a graph
"""simple docstring"""
def __init__( self , A , A , A ) -> Tuple:
'''simple docstring'''
lowerCamelCase = row
lowerCamelCase = col
lowerCamelCase = graph
def ... | 457 |
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 =logging.get_logger(__name__)
a ="""▁"""
a ={"""vocab_file""": """... | 652 | 0 |
'''simple docstring'''
import unicodedata
from dataclasses import dataclass
from typing import Optional, Union
import numpy as np
from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase
... | 43 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
lowercase__ : Optional[Any] = {
"configuration_encodec": [
"ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP",
"EncodecConfig",
]... | 43 | 1 |
import argparse
import logging
from collections import namedtuple
import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer # The authors' implementation
from transformers import BertTokenizer
logging.basicConfig(level=logging.INFO)
UpperCAmel... | 117 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_pegasus import PegasusTokenizer
else:... | 117 | 1 |
from __future__ import annotations
import collections
import pprint
from pathlib import Path
def __a ( A__ : str ):
return "".join(sorted(lowerCAmelCase__ ) )
def __a ( A__ : str ):
return word_by_signature[signature(lowerCAmelCase__ )]
... | 709 |
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_identified_filename,
... | 698 | 0 |
'''simple docstring'''
import dataclasses
import re
from dataclasses import dataclass
from functools import total_ordering
from typing import Optional, Union
_UpperCamelCase : List[Any] = re.compile(r"^(?P<major>\d+)" r"\.(?P<minor>\d+)" r"\.(?P<patch>\d+)$")
@total_ordering
@dataclass
c... | 284 |
'''simple docstring'''
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 (
... | 399 | 0 |
import operator as op
__magic_name__ = '''scaler.pt'''
__magic_name__ = '''pytorch_model'''
__magic_name__ = '''random_states'''
__magic_name__ = '''optimizer'''
__magic_name__ = '''scheduler'''
__magic_name__ = '''pytorch_model.bin'''
__magic_name_... | 705 |
from __future__ import annotations
from numpy import array, cos, cross, floataa, radians, sin
from numpy.typing import NDArray
def __magic_name__ ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ = False):
'''simple docstring'''
if radian_mode:
return [magn... | 73 | 0 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.