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"""
import argparse
import gc
import json
import os
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_wit... | 389 |
"""simple docstring"""
from collections.abc import Iterable
from typing import Any
class UpperCamelCase :
def __init__( self : str , UpperCAmelCase__ : int | None = None ) -> Tuple:
_a : List[str] = value
_a : Node | None... | 389 | 1 |
'''simple docstring'''
from abc import ABC, abstractmethod
from typing import Optional, Union
from .. import Dataset, DatasetDict, Features, IterableDataset, IterableDatasetDict, NamedSplit
from ..utils.typing import NestedDataStructureLike, PathLike
class _lowerCAmelCase ( __A ... | 411 |
'''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, req... | 411 | 1 |
'''simple docstring'''
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextModel,
CLIPTokenizer,
WhisperForConditionalGeneration,
WhisperProcessor,
)
from diffusers import (
AutoencoderKL,
DDIMSchedul... | 688 |
'''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 .to... | 688 | 1 |
"""simple docstring"""
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 lowercase__ ( ... | 711 |
"""simple docstring"""
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class lowercase__ ... | 244 | 0 |
from __future__ import annotations
def lowerCAmelCase_ ( _snake_case : int | float | str , _snake_case : int | float | str ) -> list[str]:
'''simple docstring'''
if nth_term == "":
return [""]
__magic_name__ : Tuple = int(_snake_case )
__magic_name__ ... | 124 |
import argparse
from pathlib import Path
import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version
from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging
if version.parse(fairseq.__vers... | 124 | 1 |
'''simple docstring'''
def A ( _UpperCAmelCase : int ) -> int:
'''simple docstring'''
__lowerCAmelCase : List[str] = 0
while num > 0:
digit_sum += num % 1_0
num //= 1_0
return digit_sum
def A ( _UpperCAmelCase : int... | 123 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A_ = {
"configuration_upernet": ["UperNetConfig"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except Op... | 123 | 1 |
'''simple docstring'''
# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V an... | 211 |
import gc
import unittest
from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import... | 317 | 0 |
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 tensorflow as tf
from transformers import AutoTokenizer, TFAutoModelFo... | 711 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
if is_torch_available():
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
@require_torch
@require_sentencepiece
@requ... | 450 | 0 |
import unittest
from diffusers.pipelines.pipeline_utils import is_safetensors_compatible
class lowerCAmelCase_ ( unittest.TestCase ):
def snake_case_ ( self ) -> str:
UpperCamelCase : Any = [
'safety_checker/pytorch_model.bin'... | 40 | def A ( _lowercase = 1_000_000 ):
SCREAMING_SNAKE_CASE : Optional[int] = set(range(3 , _lowercase , 2 ) )
primes.add(2 )
for p in range(3 , _lowercase , 2 ):
if p not in primes:
continue
... | 248 | 0 |
from manim import *
class a (__snake_case ):
"""simple docstring"""
def UpperCamelCase ( self : Optional[int] ) -> int:
__snake_case : List[str] = Rectangle(height=0.5 , width=0.5 )
__snake_case : ... | 719 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, LDMTextToImagePipeline, UNetaDConditionModel
from diffusers.utils.testing_utils import (
enable_full_determinism,
load_numpy,
... | 203 | 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, Enco... | 295 |
import unittest
from transformers import SqueezeBertConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tenso... | 295 | 1 |
def lowerCAmelCase__ ( _SCREAMING_SNAKE_CASE : Optional[Any] , _SCREAMING_SNAKE_CASE : Tuple ):
"""simple docstring"""
return abs(__A ) if a == 0 else greatest_common_divisor(b % a , __A )
def lowerCAmelCase__ ( _SCREAMI... | 701 |
from __future__ import annotations
from collections import Counter
from random import random
class SCREAMING_SNAKE_CASE :
def __init__( self : Optional[Any] ):
'''simple docstring'''
__a = {}
def UpperCamelCase_ ( self : Dict ,... | 547 | 0 |
'''simple docstring'''
from math import factorial
def _SCREAMING_SNAKE_CASE ( UpperCamelCase__ : int , UpperCamelCase__ : int , UpperCamelCase__ : float ):
"""simple docstring"""
if successes > trials:
raise ValueError("""s... | 442 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image
from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from ... | 442 | 1 |
import argparse
import os
import torch
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
snake_case__ : str = {
"""sample_size""": 3_2,
"""in_channels""": 3,
"""out_channels""": 3,
"""layers_per... | 618 |
import inspect
import unittest
import warnings
from math import ceil, floor
from transformers import LevitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import requ... | 618 | 1 |
"""simple docstring"""
import os
from typing import Dict, List, Union
import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs
from .tokenization_gpta import GPTaTokenizer
class lowerCAmelCase__ ( tf.keras.layers.Layer )... | 512 |
"""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 | 1 |
class _snake_case :
def __init__( self : Optional[Any], __lowercase : str = "", __lowercase : bool = False ):
# Mapping from the first character of the prefix of the node
lowercase__ = {}
# A node will be a leaf if the tree co... | 37 |
import pickle
import numpy as np
from matplotlib import pyplot as plt
class _snake_case :
def __init__( self : Tuple, __lowercase : Union[str, Any], __lowercase : int, __lowercase : Union[str, Any], __lowercase : str, __lowercase ... | 37 | 1 |
'''simple docstring'''
import tempfile
import unittest
import numpy as np
from diffusers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionPipeline,
PNDMSched... | 90 |
import math
def lowerCAmelCase_ ( _snake_case : int ) -> int:
'''simple docstring'''
if not isinstance(_snake_case , _snake_case ):
__magic_name__ : str = F'''Input value of [number={number}] must be an integer'''
raise TypeError(_snake_case )
if number ... | 124 | 0 |
"""simple docstring"""
import argparse
import struct
import unittest
class _lowerCamelCase :
def __init__( self : Tuple , lowerCamelCase_ : bytes ):
"""simple docstring"""
_lowercase : Optional[int] = data
# Initialize hash value... | 705 | """simple docstring"""
import os
from typing import Optional
import fsspec
from fsspec.archive import AbstractArchiveFileSystem
from fsspec.utils import DEFAULT_BLOCK_SIZE
class _lowerCamelCase (__lowerCamelCase ):
_snake_case = ""
_snake_case = (
None # protocol passed in p... | 283 | 0 |
'''simple docstring'''
import requests
def snake_case_ ( __snake_case : str , __snake_case : str) -> None:
lowerCAmelCase_ = {'''Content-Type''': '''application/json'''}
lowerCAmelCase_ = requests.post(__snake_case , json={'''text''': message_body} , he... | 274 | '''simple docstring'''
import collections
import inspect
import unittest
from transformers import FocalNetConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_back... | 274 | 1 |
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class __a ( __A ):
'''si... | 716 |
from __future__ import annotations
from collections import Counter
from random import random
class __a :
'''simple docstring'''
def __init__( self ):
SCREAMING_SNAKE_CASE_ : List[str] = {}
def __snake_case ( self , UpperCamelCase__ ... | 97 | 0 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from torch.backends.cuda import sdp_kernel
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
from diffusers.utils import randn_tensor, slow, torch_device
from diffuser... | 227 |
"""simple docstring"""
from timeit import timeit
def UpperCAmelCase ( snake_case : int ):
if number < 0:
raise ValueError('''the value of input must not be negative''' )
_lowerCAmelCase:str = 0
while number:
number &= number - 1
... | 227 | 1 |
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
UpperCAmelCase__ = "\\n\n"
UpperCAmelCase__ = "\nPerplexity (PPL) is one of the most common metrics for evaluating language mode... | 639 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
UpperCAmelCase__ = {
"configuration_ctrl": ["CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP", "CTRLConfig"],
"tokenization_ctrl": ["CTRLTokenizer"],
}
try:
if not is_torc... | 639 | 1 |
'''simple docstring'''
def lowercase_ ( _lowercase ) -> int:
'''simple docstring'''
if not isinstance(_lowercase , _lowercase ) or number < 0:
raise ValueError('''Input must be a non-negative integer''' )
lowerCamelCase_ : str = 0
while number... | 422 |
'''simple docstring'''
import argparse
import torch
from transformers import OpenAIGPTConfig, OpenAIGPTModel, load_tf_weights_in_openai_gpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def lowercase_ ( _lowercase , _lowercase , _low... | 422 | 1 |
import torch
from ..models.auto import AutoModelForSequenceClassification, AutoTokenizer
from .base import PipelineTool
class __UpperCamelCase ( UpperCamelCase__ ):
SCREAMING_SNAKE_CASE__ = """facebook/bart-large-mnli"""
SCREAMING_SNAKE_CASE__ = (
"""This is ... | 717 |
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 DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging
logging.set_... | 268 | 0 |
import numpy as np
import torch
import tqdm
from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler
class UpperCamelCase_ ( UpperCAmelCase__ ):
'''simple docstring'''
... | 87 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a__ = logging.get_logger(__name__)
a__ = {
"""kssteven/ibert-roberta-base""": """https://huggingface... | 477 | 0 |
"""simple docstring"""
import unittest
from knapsack import greedy_knapsack as kp
class a ( unittest.TestCase ):
def UpperCamelCase ( self : Tuple ) -> str:
lowerCamelCase_ = [10, 20, 30, 40, 50, 60]
lowerCamelCase_... | 137 |
"""simple docstring"""
def lowerCamelCase__ ( _lowerCamelCase : int = 1000 ) -> int:
lowerCamelCase_ = 3
lowerCamelCase_ = 0
while a < n:
if a % 3 == 0 or a % 5 == 0:
result += a
elif a % 15 == 0:
result -= a
a += 1
retur... | 137 | 1 |
'''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class lowerCAmelCase__ ( UpperCAmelCase_ ):
'''simple docstring'''
_lowerCamelCase =["image_processor", "tokenizer"]
_lowerCame... | 51 |
'''simple docstring'''
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version('>=', '4.25.0')):
raise OptionalDepe... | 51 | 1 |
"""simple docstring"""
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig
lowerCamelCase__ = logging.get_logger(__name__)
lowerCamelCase__ = {
"Intel/dpt-large": "https://huggingface.co/Intel/dpt-large/resolve/main/confi... | 51 |
"""simple docstring"""
import math
class __SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __SCREAMING_SNAKE_CASE ( self : Any , __a : list[list[float]] , __a : list[int] ) -> int:
_UpperCamelCase : List[Any] = 0.0
... | 51 | 1 |
'''simple docstring'''
import numpy as np
from PIL import Image
def __SCREAMING_SNAKE_CASE ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ):
"""simple docstring"""
lowercase_ : List[Any] = np.array(__lowercase )
if arr.shape[0] != arr.shape[1]:... | 620 | # flake8: noqa
# Lint as: python3
_UpperCAmelCase = [
"""VerificationMode""",
"""Version""",
"""disable_progress_bar""",
"""enable_progress_bar""",
"""is_progress_bar_enabled""",
"""experimental""",
]
from .info_utils import VerificationMode
from .logging import disabl... | 558 | 0 |
"""simple docstring"""
from manim import *
class _UpperCamelCase ( lowerCAmelCase__ ):
'''simple docstring'''
def snake_case ( self ):
__lowerCAmelCase = Rectangle(height=0.5 , width=0.5 )
__lowerCAmelCase = ... | 282 |
"""simple docstring"""
import cmath
import math
def _lowerCamelCase ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ):
'''simple docstring'''
__lowerCAmelCase = math.radians(_UpperCamelCase )
__lowerCAmelCase = math.radi... | 282 | 1 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class _lowerCAmelCase ( metaclass=SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
_lowerCamelCase = ['''torch''']
def __init__( self , *_lowerc... | 58 |
import importlib.metadata
from typing import Union
from packaging.version import Version, parse
from .constants import STR_OPERATION_TO_FUNC
lowerCamelCase : Dict =parse(importlib.metadata.version('''torch'''))
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase... | 228 | 0 |
'''simple docstring'''
from __future__ import annotations
from typing import Any
def __magic_name__ ( __UpperCAmelCase ) -> int:
'''simple docstring'''
if not postfix_notation:
return 0
snake_case_ = {'''+''', '''-''', '''*''', '''/'''}
snake_case_ ... | 593 |
'''simple docstring'''
import logging
import os
import quant_trainer
import torch
from torch.utils.data import DataLoader
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput
a : Optional[int] = logging.getLogger(__name__)
if ... | 593 | 1 |
"""simple docstring"""
from __future__ import annotations
def a ( __UpperCAmelCase : list ) -> list:
if len(__UpperCAmelCase ) == 0:
return []
__magic_name__, __magic_name__: List[str] = min(__UpperCAmelCase ), max(__U... | 96 |
'''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_sentencepi... | 207 | 0 |
class UpperCamelCase :
"""simple docstring"""
def __init__( self : Optional[int] ,_SCREAMING_SNAKE_CASE : Dict ,_SCREAMING_SNAKE_CASE : Optional[Any] ,_SCREAMING_SNAKE_CASE : List[Any] ) -> Dict:
'''simple docstring'''
A = N... | 715 |
from __future__ import annotations
from collections.abc import Generator
import requests
from bsa import BeautifulSoup
lowerCAmelCase_ = 'https://www.indeed.co.in/jobs?q=mobile+app+development&l='
def snake_case ( UpperCAmelCase : str = "mumbai" ):
A = BeautifulS... | 110 | 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_tensor, rand... | 675 |
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
__A = 'scheduler_config.json'
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""... | 484 | 0 |
'''simple docstring'''
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_con... | 715 | '''simple docstring'''
import numpy as np
from scipy.spatial.distance import cdist
from sklearn.metrics import fa_score
import datasets
UpperCamelCase : int = """\
@inproceedings{kakwani2020indicnlpsuite,
title={{IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Mu... | 610 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase = logging.get_logger(__name__)
_lowercase = {
'''SCUT-DLVCLab/lilt-roberta-en-base''': (
'''https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/main/config.js... | 91 |
"""simple docstring"""
# HF Trainer benchmarking tool
#
# This tool can be used to run and compare multiple dimensions of the HF Trainers args.
#
# It then prints a report once in github format with all the information that needs to be shared
# with others and second time in a console-friendly format, so it's easier... | 91 | 1 |
'''simple docstring'''
import os
import re
import shutil
import sys
import tempfile
import unittest
import black
__lowerCamelCase : str = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))
import check_copi... | 711 |
import inspect
import unittest
from datasets import load_dataset
from packaging import version
from transformers import BeitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.uti... | 457 | 0 |
"""simple docstring"""
import os
import random
import sys
from . import cryptomath_module as cryptomath
from . import rabin_miller
lowerCamelCase__ : List[Any] = 3
def __A ( a_ : int )-> int:
'''simple docstring'''
print('''Generating primitive root of p''' )
... | 698 |
"""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 | 1 |
"""simple docstring"""
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, BlipaP... | 293 |
"""simple docstring"""
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging
UpperC... | 293 | 1 |
"""simple docstring"""
from .glue import GlueDataset, GlueDataTrainingArguments
from .language_modeling import (
LineByLineTextDataset,
LineByLineWithRefDataset,
LineByLineWithSOPTextDataset,
TextDataset,
TextDatasetForNextSentencePrediction,
)
from .squad import SquadDataset, SquadDataTrainingAr... | 91 |
from jiwer import compute_measures
import datasets
lowerCamelCase :Any = '\\n@inproceedings{inproceedings,\n author = {Morris, Andrew and Maier, Viktoria and Green, Phil},\n year = {2004},\n month = {01},\n pages = {},\n title = {From WER and RIL to MER and WIL: improved evaluation ... | 487 | 0 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_A : Tuple = logging.get_logger(__name__)
_A : str = {
'''bert-base-uncased''': '''https://hugging... | 710 |
from __future__ import annotations
from typing import Any
class a :
def __init__( self : str , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : float = 0 ):
__lowerCamelCase , __lowerCamelCase: ... | 189 | 0 |
'''simple docstring'''
import argparse
import json
import os
import torch
from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def __SCREAMING_SNAKE_CASE ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE... | 585 |
import argparse
import importlib
from pathlib import Path
# Test all the extensions added in the setup
lowerCamelCase : str = [
'''kernels/rwkv/wkv_cuda.cu''',
'''kernels/rwkv/wkv_op.cpp''',
'''kernels/deformable_detr/ms_deform_attn.h''',
'''kernels/deformable_detr/cuda/ms_de... | 367 | 0 |
"""simple docstring"""
def a__ ( lowerCAmelCase : dict ):
'''simple docstring'''
UpperCAmelCase__ : Tuple = set()
# edges = list of graph's edges
UpperCAmelCase__ : Tuple = get_edges(lowerCAmelCase )
# While there are still eleme... | 718 |
"""simple docstring"""
import warnings
from pathlib import Path
from typing import List, Tuple, Union
import fire
from torch import nn
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer, PreTrainedModel
from transformers.utils import logging
A__ : Tuple = logging.get_logger(__name__... | 660 | 0 |
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
snake_case : str = (3, 9, -11, 0, 7, 5, 1, -1)
snake_case : List[str] = (4, 6, 2, 0, 8, 10, 3, -2)
@dataclass
class _snake_case :
UpperCamelCase__ = ... | 124 |
def SCREAMING_SNAKE_CASE_ ( UpperCamelCase__ = 2_0_0_0_0_0_0 ):
UpperCamelCase__ : Dict = [0 for i in range(n + 1 )]
UpperCamelCase__ : Any = 1
UpperCamelCase__ : int = 1
for i in range(2 , int(n**0.5 ) + 1 ):
... | 285 | 0 |
'''simple docstring'''
def __lowerCAmelCase ( snake_case__ ):
if n == 1 or not isinstance(snake_case__ , snake_case__ ):
return 0
elif n == 2:
return 1
else:
__UpperCamelCase : str = [0, 1]
for i in range(2 , n +... | 399 |
'''simple docstring'''
from typing import Any
import numpy as np
def __lowerCAmelCase ( snake_case__ ):
return np.array_equal(snake_case__ , matrix.conjugate().T )
def __lowerCAmelCase ( snake_case__ , snake_case__ ):
__UpperCamelCase ... | 399 | 1 |
from __future__ import annotations
import inspect
import unittest
from math import floor
import numpy as np
from transformers import CvtConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from... | 520 | import os
import shutil
from pathlib import Path
from typing import Optional, Union
import numpy as np
from huggingface_hub import hf_hub_download
from ..utils import ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME, is_onnx_available, logging
if is_onnx_available():
import onnxruntime as ort
Upp... | 520 | 1 |
import re
def UpperCAmelCase__ ( lowerCamelCase ):
lowercase :Any = re.compile(
r"^(?:0|94|\+94|0{2}94)" r"7(0|1|2|4|5|6|7|8)" r"(-| |)" r"\d{7}$" )
return bool(re.search(lowerCamelCase, lowerCamelCase ) )
if __name__ == "__main__":
_UpperCAmelCase : Opt... | 712 |
def UpperCAmelCase__ ( lowerCamelCase, lowerCamelCase ):
if discount_rate < 0:
raise ValueError("Discount rate cannot be negative" )
if not cash_flows:
raise ValueError("Cash flows list cannot be empty" )
lowercase :Dict = sum(
cash_flow / ((1 + discount_rate) *... | 453 | 0 |
"""simple docstring"""
import warnings
from ...utils import is_sklearn_available, requires_backends
if is_sklearn_available():
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef
__lowerCAmelCase : List[str] =(
"""... | 359 | """simple docstring"""
from copy import deepcopy
class _A :
def __init__( self , __lowerCAmelCase = None , __lowerCAmelCase = None ):
"""simple docstring"""
if arr is None and size is not None:
lowerc... | 359 | 1 |
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 ModelTesterMixin, id... | 473 |
import itertools
import string
from collections.abc import Generator, Iterable
def _lowerCAmelCase ( _lowerCAmelCase , _lowerCAmelCase ) -> Generator[tuple[str, ...], None, None]:
'''simple docstring'''
__snake_case = iter(_lowerCAmelCase )
... | 473 | 1 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class _lowercase ( metaclass=__lowercase ):
_SCREAMING_SNAKE_CASE : List[Any] = ["onnx"]
def __init__( self : Dict , *SCREAMING_SNAKE_CASE_ : Union[str, Any] , **SCRE... | 56 |
"""simple docstring"""
def A ( _A = 100 ):
"""simple docstring"""
snake_case_ :int = set()
snake_case_ :Dict = 0
snake_case_ :str = n + 1 # maximum limit
for a in range(2, _A ):
for b in range(2, _A ):
... | 584 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase = {'''configuration_ibert''': ['''IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''IBertConfig''', '''IBertOnnxConfig''']}
try:
if not is_torch_available... | 569 |
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 __lowerCamelCase ( snake_case__ ,snake_case__ ,snake_case__ ,snake_case__ ,snake_case__ ) -> float:
... | 569 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase_ = {"""configuration_focalnet""": ["""FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FocalNetConfig"""]}
... | 314 |
from typing import Any
import numpy as np
def SCREAMING_SNAKE_CASE__ ( snake_case__ :np.ndarray ) -> bool:
return np.array_equal(snake_case__ , matrix.conjugate().T )
def SCREAMING_SNAKE_CASE__ ( snake_case__ :np.ndarray , snake_case__ :np.ndarray ) ... | 67 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A = logging.get_logger(__name__)
_A = {
'microsoft/swinv2-tiny-patch4-window8-256': (
'https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256/resolve/... | 710 | """simple docstring"""
from typing import Optional, Tuple, Union
import torch
from einops import rearrange, reduce
from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
f... | 538 | 0 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_chan... | 120 |
'''simple docstring'''
from __future__ import annotations
from collections import namedtuple
from dataclasses import dataclass
@dataclass
class a :
'''simple docstring'''
__lowerCAmelCase : int
__lowerCAmelCase : TreeNode | None = None
__lowerCAmelCase : TreeNode | ... | 120 | 1 |
'''simple docstring'''
def snake_case_ ( a__ : Dict ):
"""simple docstring"""
__lowercase = []
__lowercase = set({"""(""", """[""", """{"""} )
__lowercase = set({""")""", """]""", """}"""} )
__lowercase = ... | 706 |
'''simple docstring'''
import re
from typing import Callable, List, Optional, Union
import tensorflow as tf
try:
from tensorflow.keras.optimizers.legacy import Adam
except ImportError:
from tensorflow.keras.optimizers import Adam
class SCREAMING_SNAKE_CASE( tf.keras.optimi... | 163 | 0 |
"""simple docstring"""
from math import isqrt, loga
def _snake_case ( __snake_case : int ):
"""simple docstring"""
_lowerCamelCase : List[str] = [True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:... | 88 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_A = {"""configuration_sew""": ["""SEW_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SEWConfig"""]}
try:
if not is_torch_available():
raise OptionalDependencyNotAv... | 182 | 0 |
"""simple docstring"""
def __UpperCAmelCase ( _snake_case : list, _snake_case : list, _snake_case : int ):
if len(_snake_case ) != len(_snake_case ):
raise ValueError("The length of profit and weight must be same." )
if max_weight <= 0:
raise Value... | 227 | """simple docstring"""
import argparse
import datetime
def __UpperCAmelCase ( _snake_case : str ):
_lowercase = {
"0": "Sunday",
"1": "Monday",
"2": "Tuesday",
"3": "Wednesday",
"4": "Thursday",
"5": "Friday",
"6": "Saturday",
}
... | 227 | 1 |
"""simple docstring"""
def snake_case__ ( _snake_case : int = 50 ):
"""simple docstring"""
UpperCamelCase__ = [[0] * 3 for _ in range(length + 1 )]
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ... | 516 | """simple docstring"""
def snake_case__ ( _snake_case : int ):
"""simple docstring"""
if number > 0:
raise ValueError("input must be a negative integer" )
UpperCamelCase__ = len(bin(_snake_case )[3:] )
UpperCamelCase__ ... | 516 | 1 |
"""simple docstring"""
from math import sqrt
def A_ ( _lowercase ):
'''simple docstring'''
snake_case_ :Tuple = 0
for i in range(1, int(sqrt(_lowercase ) + 1 ) ):
if n % i == 0 and i != sqrt(_lowercase ):
total += i + n // i... | 710 |
"""simple docstring"""
import argparse
import os
import torch
from transformers import FlavaImageCodebook, FlavaImageCodebookConfig
def A_ ( _lowercase, _lowercase, _lowercase, _lowercase ):
'''simple docstring'''
snake_case_ :Any = s.rsplit(_lowercase, ... | 310 | 0 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device
UpperCAmelCase__ : str = False
class A ( unittest.TestCase ... | 48 |
"""simple docstring"""
import unittest
from parameterized import parameterized
from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...te... | 231 | 0 |
"""simple docstring"""
import unittest
import numpy as np
import requests
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... | 716 |
"""simple docstring"""
import argparse
import logging
import os
from datetime import datetime
import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from tqdm import tqdm
from transformers import GPTaLMHeadModel
_A = logging.getLo... | 507 | 0 |
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel
from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import HeunDiscreteSchedul... | 272 |
import collections
import inspect
import unittest
from transformers import FocalNetConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import Backb... | 272 | 1 |
"""simple docstring"""
from dataclasses import dataclass, field
from typing import Optional
from transformers import AutoConfig, AutoImageProcessor, AutoTokenizer, FlaxVisionEncoderDecoderModel, HfArgumentParser
@dataclass
class lowerCAmelCase :
'''simple docstring'''
... | 703 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__UpperCamelCase = {
'''configuration_roformer''': ... | 327 | 0 |
from typing import List
import datasets
from datasets.tasks import AudioClassification
from ..folder_based_builder import folder_based_builder
lowerCAmelCase_ = datasets.utils.logging.get_logger(__name__)
class __lowerCAmelCase ( folder_based_builder.FolderBasedBuilderConfig ):
low... | 60 |
"""simple docstring"""
# Function to print upper half of diamond (pyramid)
def _lowercase ( __snake_case ) -> Dict:
for i in range(0 ,__snake_case ):
for _ in range(0 ,n - i - 1 ): # printing spaces
print(" " ,end="" )
for ... | 293 | 0 |
"""simple docstring"""
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICE... | 621 |
"""simple docstring"""
import argparse
import torch
from transformers import GPTaLMHeadModel, RobertaForMaskedLM
if __name__ == "__main__":
a_ = argparse.ArgumentParser(
description=(
"Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for... | 621 | 1 |
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 ...feature... | 398 |
a : Any = [
'''Audio''',
'''Array2D''',
'''Array3D''',
'''Array4D''',
'''Array5D''',
'''ClassLabel''',
'''Features''',
'''Sequence''',
'''Value''',
'''Image''',
'''Translation''',
'''TranslationVariableLanguages''',
]
from .audio import Audio
from .f... | 639 | 0 |
import builtins
import sys
from ...utils.imports import _is_package_available
from . import cursor, input
from .helpers import Direction, clear_line, forceWrite, linebreak, move_cursor, reset_cursor, writeColor
from .keymap import KEYMAP
_lowerCamelCase =False
try:
_lowerCamelCase =_is_package_avail... | 252 |
import argparse
from typing import List
import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset
# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
from torch.optim import AdamW
from torch.utils.data import DataLoa... | 252 | 1 |
def _A ( ) -> list[list[int]]:
"""simple docstring"""
return [list(range(10_00 - i , -10_00 - i , -1 ) ) for i in range(10_00 )]
__snake_case = generate_large_matrix()
__snake_case = (
[[4, 3, 2, -1], [3, 2, 1, -1], [1, 1, -1, -2], [-1, ... | 1 |
import math
import tensorflow as tf
from packaging import version
def lowerCAmelCase_ ( lowerCamelCase ):
__magic_name__ : str =tf.convert_to_tensor(lowerCamelCase )
__magic_name__ : List[str] =0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0... | 21 | 0 |
"""simple docstring"""
import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image
if version.parse(version.parse(PIL.__version__).base_version) >= version.parse('9.1.0'):
SCREAMING_SNAKE_CASE__ : Any ={
'linear': PIL.Image.Resampling.BILINEAR,
... | 718 | """simple docstring"""
import shutil
import tempfile
import unittest
from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio
from .test_feature_extraction_clap import floats_list
... | 558 | 0 |
"""simple docstring"""
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Image
from .base import TaskTemplate
@dataclass(frozen=a_ )
class _SCREAMING_SNAKE_CASE ( a_ ):
"""simple docstring"""
_a : Un... | 200 |
'''simple docstring'''
import warnings
from typing import Dict
import numpy as np
from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF... | 349 | 0 |
'''simple docstring'''
import numpy as np
import torch
from torch.utils.data import Dataset
from utils import logger
class __lowercase (__lowerCamelCase ):
def __init__( self : Dict , UpperCAmelCase_ : List[str] , UpperCAmelCase_ ... | 6 |
'''simple docstring'''
import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize
def __UpperCAmelCase ( lowerCamelCase_) -> float:
return np.dot(lowerCamelCase_ , lowerCamelCase_)
cl... | 6 | 1 |
# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import flax
import jax
import jax.numpy as jnp
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
... | 300 |
from __future__ import annotations
def lowerCamelCase__ ( _a):
if len(_a) == 0:
return []
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : Tuple = min(_a), max(_a)
SCREAMING_SNAKE_CASE : Dict = int(max_value - min_value) + 1
SCREAMING_SNAKE_CASE : list[list] = ... | 25 | 0 |
def a_ ( _A , _A ) -> float:
"""simple docstring"""
if density <= 0:
raise ValueError('Impossible fluid density' )
if bulk_modulus <= 0:
raise ValueError('Impossible bulk modulus' )
return (bulk_modulus / density) ** 0.5
... | 372 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModel,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import AutoencoderKL, DDIMSchedule... | 372 | 1 |
'''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/config.json",
# See all WavLM m... | 109 | import os
import re
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
A_ = logging.get_logger(__name__)
A_ = {
"vocab_file": "vocab.txt",
"merges_file": "bpe.codes",
}
A_ ... | 604 | 0 |
'''simple docstring'''
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, UNetaDConditionMode... | 712 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase_ : Dict = logging.get_logger(__name__)
lowerCAmelCase_ : Union[s... | 521 | 0 |
from typing import Any
class snake_case_ :
'''simple docstring'''
def __init__( self : Dict , __lowerCamelCase : Optional[Any] ) -> Dict:
'''simple docstring'''
__lowercase = data
__lowercase = None
def __... | 375 |
def lowerCAmelCase ( UpperCAmelCase, UpperCAmelCase ) ->list:
"""simple docstring"""
__magic_name__ : Optional[Any] = word.split()
def justify(UpperCAmelCase, UpperCAmelCase, UpperCAmelCase ) -> str:
__mag... | 154 | 0 |
import argparse
import fairseq
import torch
from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging
logging.set_verbosity_info()
__lowerCamelCase = logging.get_logger(__name__)
__lowerCamelCase = {
'''post_extract_proj''': '''feature_project... | 478 |
from __future__ import annotations
from fractions import Fraction
from math import gcd, sqrt
def _a ( __UpperCamelCase ):
a_ : int = int(number**0.5 )
return number == sq * sq
def _a ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ... | 478 | 1 |
"""simple docstring"""
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : int ):
if num <= 0:
raise ValueError('Input must be a positive integer' )
lowerCAmelCase = [True] * (num + 1)
lowerCAmelCase = 2
while p * p <= num:
if primes[p]:
for i in range(p * p , num + 1 , ... | 4 |
import argparse
import hashlib # hashlib is only used inside the Test class
import struct
class UpperCAmelCase_ :
def __init__( self , _lowerCAmelCase ):
UpperCAmelCase__ : Any = data
UpperCAmelCase__ : List[Any] ... | 79 | 0 |
import os
from distutils.util import strtobool
def __lowerCAmelCase ( __magic_name__ , __magic_name__ ):
for e in env_keys:
_lowercase: List[Any] = int(os.environ.get(__magic_name__ , -1 ) )
if val >= 0:
return val
return default
def __lowerCAmelCase ... | 720 |
from typing import List, Union
import numpy as np
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_t... | 206 | 0 |
from __future__ import annotations
def snake_case (UpperCAmelCase__ , UpperCAmelCase__ ) -> list[str]:
if nth_term == "":
return [""]
UpperCamelCase_: List[str] = int(UpperCAmelCase__ )
UpperCamelCase_: Any = int(UpperCAmelCase_... | 57 |
"""simple docstring"""
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.Test... | 636 | 0 |
"""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
... | 442 |
"""simple docstring"""
from collections.abc import Callable
import numpy as np
def lowerCAmelCase_ ( UpperCamelCase__ : Callable , UpperCamelCase__ : float , UpperCamelCase__ : float , UpperCamelCase__ : float , UpperCamelCase__ : float ):
"""simple docstring""... | 442 | 1 |
'''simple docstring'''
import os
import unittest
from transformers import LxmertTokenizer, LxmertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
... | 143 |
'''simple docstring'''
from math import factorial
class _snake_case :
def __init__( self : Optional[int] ,SCREAMING_SNAKE_CASE__ : Tuple ,SCREAMING_SNAKE_CASE__ : List[str] ):
SCREAMING_SNAKE_CASE:str = real
if isinstance(SCREAMING_SNAKE_CA... | 143 | 1 |
"""simple docstring"""
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_docs... | 702 |
"""simple docstring"""
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase = logging.get_logger(__name__)
lowercase = {
"""BAAI/AltCLIP""": """https://huggingface.co/BAAI/AltCLIP/resolve/main/config.json""... | 150 | 0 |
"""simple docstring"""
class _snake_case :
"""simple docstring"""
def __init__( self : Optional[Any] , _A : Tuple):
"""simple docstring"""
_SCREAMING_SNAKE_CASE : Optional[int] = arr.split(""",""")
def _lowerCAmel... | 338 |
'''simple docstring'''
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, T... | 527 | 0 |
import sys
import turtle
def _lowerCamelCase( lowerCAmelCase__ : tuple[float, float] , lowerCAmelCase__ : tuple[float, float] ):
'''simple docstring'''
return (pa[0] + pa[0]) / 2, (pa[1] + pa[1]) / 2
def _lowerCamelCase( lowerCAmelCase__ : tuple[floa... | 704 |
import math
def _lowerCamelCase( lowerCAmelCase__ : float , lowerCAmelCase__ : float ):
'''simple docstring'''
return math.pow(lowerCAmelCase__ , 2 ) - a
def _lowerCamelCase( lowerCAmelCase__ : float ):
'''simple docstring'''... | 97 | 0 |
from __future__ import annotations
import requests
def __snake_case ( lowerCAmelCase_ ) -> dict:
SCREAMING_SNAKE_CASE__ = f'''https://hacker-news.firebaseio.com/v0/item/{story_id}.json?print=pretty'''
return requests.get(lowerCAmelCase_ ).json()
def __snake_... | 100 |
"""simple docstring"""
def lowercase ( __snake_case : Tuple ):
lowercase_ : Any = 0
lowercase_ : Union[str, Any] = len(__snake_case )
for i in range(n - 1 ):
for j in range(i + 1 , __snake_case ):
... | 231 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
UpperCamelCase : Dict = {"configuration_plbart": ["PLBART_PRETRAINED... | 701 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase : Tuple = logging.get_logger(__name__)
UpperCamelCase : Union[str, Any] = {
"SCUT-DLVCLab/lilt-roberta-en-base": (
"https://hugging... | 91 | 0 |
"""simple docstring"""
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/... | 118 |
"""simple docstring"""
from __future__ import annotations
from random import random
class __a :
'''simple docstring'''
def __init__( self , _lowerCamelCase = None ) -> str:
'''simple docstring'''
__lowe... | 118 | 1 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import AlbertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.n... | 320 | '''simple docstring'''
import argparse
from pathlib import Path
import torch
from packaging import version
from torch.onnx import export
from diffusers import AutoencoderKL
UpperCamelCase_ = version.parse(version.parse(torch.__version__).base_version) < version.parse('''1.11''')
def lowerCamelCa... | 320 | 1 |
import warnings
from functools import wraps
from typing import Callable
def __a ( lowerCAmelCase_ : Callable ) -> Dict:
'''simple docstring'''
@wraps(__lowerCamelCase )
def _inner_fn(*lowerCAmelCase_ : int ,**lowerCAmelCase_ : Optional[Any] )... | 593 |
'''simple docstring'''
import hashlib
import unittest
from typing import Dict
import numpy as np
from transformers import (
MODEL_FOR_MASK_GENERATION_MAPPING,
TF_MODEL_FOR_MASK_GENERATION_MAPPING,
is_vision_available,
pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from ... | 446 | 0 |
'''simple docstring'''
from __future__ import annotations
def lowerCamelCase__ ( __lowerCamelCase : list[int] , __lowerCamelCase : int , __lowerCamelCase : int , __lowerCamelCase : int ):
'''simple docstring'''
if (direction == 1 and array[ind... | 331 |
'''simple docstring'''
from transformers import BertTokenizerFast
from .custom_tokenization import CustomTokenizer
class __magic_name__ ( lowerCAmelCase ):
UpperCAmelCase =CustomTokenizer
pass
| 331 | 1 |
import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conver... | 583 |
import os
import tempfile
import unittest
from transformers import NezhaConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ... | 416 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
'''google/canine-s''': '''https://huggingface.co/google/canine-s/resolve/main/config.json''',
# See all CANINE models at https:... | 706 |
'''simple docstring'''
from ....configuration_utils import PretrainedConfig
from ....utils import logging
lowercase_ = logging.get_logger(__name__)
# TODO: upload to AWS
lowercase_ = {
"yjernite/retribert-base-uncased": (
"https://huggingface.co/yjernite/retribert-base-uncased/resolv... | 352 | 0 |
import socket
def __lowercase( ):
"""simple docstring"""
lowerCamelCase = socket.socket(socket.AF_INET , socket.SOCK_STREAM )
lowerCamelCase = socket.gethostname()
lowerCamelCase = 12312
sock.connect((host, port... | 623 |
import argparse
import json
import os
import torch
from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def __lowercase( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCA... | 623 | 1 |
UpperCamelCase__ : int = {"a": ["c", "b"], "b": ["d", "e"], "c": [], "d": [], "e": []}
UpperCamelCase__ : List[str] = ["a", "b", "c", "d", "e"]
def A_( A , A , A ):
UpperCAmelCase_ = start
# add current to visited
visited.append(__snake_cas... | 713 |
import fire
from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer
def A_( A , A , **A ):
UpperCAmelCase_ = AutoConfig.from_pretrained(A , **A )
UpperCAmelCase_ = AutoModelForSeqaSeqLM.from_config(A )
model.save_pr... | 486 | 0 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaInpaintPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils ... | 421 |
'''simple docstring'''
from jiwer import compute_measures
import datasets
snake_case_ = '\\n@inproceedings{inproceedings,\n author = {Morris, Andrew and Maier, Viktoria and Green, Phil},\n year = {2004},\n month = {01},\n pages = {},\n title = {From WER and RIL to MER and W... | 421 | 1 |
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',
'MobileNetV2Config',
'MobileNetV2On... | 387 | import dataclasses
import re
import string
from typing import Any, Dict, Iterator, List, Mapping, Optional, Sequence, Tuple
import numpy as np
from . import residue_constants
UpperCamelCase = Mapping[str, np.ndarray]
UpperCamelCase = Mapping[str, Any] # Is a nested dict.
UpperCamelCas... | 387 | 1 |
'''simple docstring'''
def snake_case ( a_ : Optional[Any] ) -> Any:
"""simple docstring"""
UpperCamelCase_ : Union[str, Any] = len(a_ )
while cur > 1:
# Find the maximum number in arr
UpperCamelCase_ : Lis... | 208 |
'''simple docstring'''
from typing import List, Optional, Union
import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, Te... | 208 | 1 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_a: str = {"""configuration_focalnet""": ["""FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FocalNetConfig"""]}
try:
if not is_torch_available(... | 720 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_a: List[str] = logging.get_logger(__name__)
_a: Any = {
"""sayakpaul/vit-msn-base""": """https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json""",
# See all ViT MSN models at https://h... | 268 | 0 |
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