code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
import inspect
import unittest
from transformers import MobileNetVaConfig
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_configuration_common import ConfigTester
from ..... | 300 |
import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging
_lowerCamelCase : List[str] = logging.get... | 282 | 0 |
import torch
from diffusers import DDIMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class snake_case_ ( __A ):
__A : Dict = (DDIMParallelScheduler,)
__A : int = (("eta", 0.0), ("num_inference_steps", 50))
def __UpperCa... | 333 | from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase = logging.get_logger(__name__)
UpperCamelCase = {
'''RWKV/rwkv-4-169m-pile''': '''https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json''',
'''RWKV/rwkv-4-430m-pil... | 333 | 1 |
"""simple docstring"""
import argparse
from pathlib import Path
import torch
from transformers import OPTConfig, OPTModel
from transformers.utils import logging
logging.set_verbosity_info()
lowerCAmelCase__ = logging.get_logger(__name__)
def a__ ( SCREAMING_SNAKE_CASE : Dict ... | 108 |
"""simple docstring"""
from .glue import GlueDataset, GlueDataTrainingArguments
from .language_modeling import (
LineByLineTextDataset,
LineByLineWithRefDataset,
LineByLineWithSOPTextDataset,
TextDataset,
TextDatasetForNextSentencePrediction,
)
from .squad import SquadDataset, S... | 78 | 0 |
def SCREAMING_SNAKE_CASE ( lowercase_ = 50 ) -> int:
"""simple docstring"""
A__ = [1] * (length + 1)
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_start in range(row_length - tile_le... | 231 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCamelCase : str = {"""configuration_focalnet""": ["""FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FocalNetConfig"""]}
try:
if no... | 231 | 1 |
import logging
import os
from typing import List, Tuple
import numpy as np
import psutil
import torch
import torch.distributed as dist
from transformers import RagRetriever
A_ : Dict = logging.getLogger(__name__)
class _a (A__ ):
'''simple docstring'''
def __init__... | 192 |
import enum
import os
from hashlib import shaaaa
from typing import Optional
from .. import config
from .logging import get_logger
a_ : List[Any] = get_logger(__name__)
class _snake_case ( enum.Enum ):
_lowercase : Any = '''all_checks'''
_lowercase ... | 137 | 0 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import ... | 370 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless require... | 315 | 0 |
'''simple docstring'''
import inspect
import unittest
from typing import List
import numpy as np
from transformers import EfficientFormerConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
... | 55 |
'''simple docstring'''
import copy
import re
class A__ :
A__ = 'hp'
A__ = {}
A__ = None
@classmethod
def A ( cls : Optional[Any] , _a : Optional[Any] , _a : Any ) -> Union[str, Any]:
'''simple docstring'''
... | 47 | 0 |
'''simple docstring'''
import math
import time
from typing import Dict, List, Optional
from torch.utils.data import Dataset
from transformers import SeqaSeqTrainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
i... | 353 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps
from ..pipeline_... | 236 | 0 |
import torch
from diffusers import DDIMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class A_ ( _a ):
'''simple docstring'''
a__ = (DDIMParallelScheduler,)
a__ = (("eta", 0.0), ("num_inference_steps", 50))
def ... | 333 |
import math
from enum import Enum
from typing import Optional, Union
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from .utils import logging
A_ : Tuple = logging.get_logger(__name__)
class A_ ( _a ):
'''simple docstring'''
... | 333 | 1 |
# Logistic Regression from scratch
# In[62]:
# In[63]:
# importing all the required libraries
import numpy as np
from matplotlib import pyplot as plt
from sklearn import datasets
def __lowercase ( lowerCamelCase : Union[str, Any] ):
return 1 / (1 + np.exp(-z ))
def __lo... | 356 | def __lowercase ( lowerCamelCase : list[int] ):
if not numbers:
return 0
if not isinstance(lowerCamelCase , (list, tuple) ) or not all(
isinstance(lowerCamelCase , lowerCamelCase ) for number in numbers ):
raise ValueError('numbers must be an iterable of integers' ... | 50 | 0 |
import argparse
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedTyp... | 231 |
from ..utils import DummyObject, requires_backends
class _lowerCAmelCase ( metaclass=__a ):
_lowercase =['''torch''']
def __init__( self , *_UpperCamelCase , **_UpperCamelCase ) -> Dict:
requires_backends(self , ["torch"] )
@classmeth... | 231 | 1 |
"""simple docstring"""
import logging
import math
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union
import torch
from .tensor_utils import tensor_tree_map, tree_map
def _lowerCamelCase( lowercase__ ) -> List[Tuple[int... | 367 |
from __future__ import annotations
import numpy as np
def _lowerCamelCase( lowercase__ ) -> str:
'''simple docstring'''
return np.maximum(0 , lowercase__ )
if __name__ == "__main__":
print(np.array(relu([-1, 0, 5]))) # --> [0, 0, 5]
| 304 | 0 |
from collections.abc import Sequence
def lowerCAmelCase_ ( snake_case_,snake_case_ ):
return sum(c * (x**i) for i, c in enumerate(_snake_case ) )
def lowerCAmelCase_ ( snake_case_,snake_case_ ):
_A : Any = 0.0
for coeff in reversed(_s... | 26 |
"""simple docstring"""
from scipy.stats import spearmanr
import datasets
a = '''
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no correlation.
Positive correlati... | 315 | 0 |
"""simple docstring"""
import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation
def _snake_case ( _snake_case : int = 8 ) -> str:
'''simple docstring'''
_A = ascii_letters + digi... | 356 |
"""simple docstring"""
from __future__ import annotations
import time
import numpy as np
a = [8, 5, 9, 7]
a = [
[2, 0, 1, 1],
[0, 1, 2, 1],
[4, 0, 0, 3],
[0, 2, 1, 0],
[1, 0, 3, 0],
]
a = [
[3, 2, 1, 4],
[0, 2, 5, 2],
[5, 1, 0, 5],
[1, ... | 271 | 0 |
import logging
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
BERT_START_DOCSTRING,
BertEncoder... | 107 |
def UpperCAmelCase__ ( lowerCamelCase, lowerCamelCase ):
lowercase :List[str] = ""
for word_or_phrase in separated:
if not isinstance(lowerCamelCase, lowerCamelCase ):
raise Exception("join() accepts only strings to be joined" )
joined += word_or_phrase + separa... | 236 | 0 |
from __future__ import annotations
import unittest
from transformers import BlenderbotSmallConfig, BlenderbotSmallTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import Confi... | 350 |
def SCREAMING_SNAKE_CASE__ ( snake_case_, snake_case_ ) -> int:
"""simple docstring"""
a = ''''''
for i in table:
res += inp[i - 1]
return res
def SCREAMING_SNAKE_CASE__ ( snake_case_ ) -> int:
"""simple docstring"""
... | 330 | 0 |
import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional
import pyarrow as pa
import pyarrow.json as paj
import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline
_snake_case = datasets.utils.logging.get_logge... | 26 |
import argparse
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
_UpperCAmelCase : Dict ... | 50 | 0 |
import unittest
import numpy as np
from transformers import is_flax_available
from transformers.testing_utils import require_flax
from ..test_modeling_flax_common import ids_tensor
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.generation import (
FlaxForcedBOSTokenL... | 231 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_lowerCamelCase : Union[str, Any] = """▁"""
_lowerCamelCase : Optional[Any] = ... | 231 | 1 |
from manim import *
class SCREAMING_SNAKE_CASE__ ( lowercase__ ):
def SCREAMING_SNAKE_CASE ( self : Any ) -> Optional[int]:
a_ : Optional[int] = Rectangle(height=0.5 , width=0.5 )
a_ : List[Any] = R... | 32 |
'''simple docstring'''
def __UpperCAmelCase ( A : int ) -> list:
# bit count represents no. of bits in the gray code
if bit_count < 0:
raise ValueError('''The given input must be positive''' )
# get the generated string sequence
UpperCAmelCase_ : int = gr... | 304 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__a: Tuple = {
"""configuration_pegasus_x""": ["""PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP""", """PegasusXConfig"""],
}
try:
if not is_torch_available():
... | 214 | '''simple docstring'''
def __UpperCamelCase ( ):
lowercase__ : Any = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]
lowercase__ : Any = 6
lowercase__ : Optional[Any] = 1
lowercase__ : int = 1901
lowercase__ : List[str] = 0
while year < 2001:
da... | 214 | 1 |
"""simple docstring"""
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTest... | 109 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Iterator
from typing import Generic, TypeVar
__lowerCAmelCase = TypeVar("""T""")
class UpperCAmelCase__ ( Generic[T] ):
"""simple docstring"""
def __init__( self : Tuple ,_a :... | 271 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ = logging.get_logger(__name__)
A_ = {
'''microsoft/cvt-13''': '''https://huggingface.co/microsoft/cvt-13/resolve/main/config.json''',
# See all Cvt models at https://hugg... | 365 |
"""simple docstring"""
import unittest
from transformers import MraConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random... | 296 | 0 |
from .imports import is_rich_available
if is_rich_available():
from rich.traceback import install
install(show_locals=False)
else:
raise ModuleNotFoundError('''To use the rich extension, install rich with `pip install rich`''')
| 340 |
from __future__ import annotations
from cmath import sqrt
def _a ( UpperCamelCase_ : int , UpperCamelCase_ : int , UpperCamelCase_ : int ) -> tuple[complex, complex]:
"""simple docstring"""
if a == 0:
raise ValueError("Coefficient... | 340 | 1 |
import doctest
from collections import deque
import numpy as np
class lowercase__ :
def __init__( self : Optional[int] ):
SCREAMING_SNAKE_CASE__ = [2, 1, 2, -1]
SCREAMING_SNAKE_CASE__ = [1, 2, 3, 4]
def A_ ( self : ... | 169 |
import math
def _lowercase ( UpperCamelCase_ , UpperCamelCase_ ) -> float:
'''simple docstring'''
if initial_intensity < 0:
raise ValueError('The value of intensity cannot be negative' )
# handling of negative values of initial intensity
if angle ... | 169 | 1 |
import argparse
import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers import (
CLIPTokenizer,
CLIPTokenizerFast,
VideoMAEImageProcessor,
XCLIPConfig,
XCLIPModel,
XCLIPProcessor,
XCLIPTextConfig,
XCLIPVisionConfig,
)
def low... | 231 |
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging
logging.set_verbosity... | 231 | 1 |
from __future__ import annotations
class SCREAMING_SNAKE_CASE__ :
def __init__( self : Optional[int] , __A : int ):
snake_case__ : Union[str, Any] = order
# a_{0} ... a_{k}
snake_case__ : Union[str, Any] = [1.0] + [0.0] * order
... | 354 |
__lowerCamelCase : Optional[int] = """Tobias Carryer"""
from time import time
class SCREAMING_SNAKE_CASE__ :
"""simple docstring"""
def __init__( self : List[Any] , __A : List[Any] , __A : Optional[int] , __A : List[st... | 286 | 0 |
from collections import defaultdict
from math import gcd
def snake_case__ ( SCREAMING_SNAKE_CASE_ : int = 1_500_000 ):
'''simple docstring'''
lowercase__ : defaultdict = defaultdict(SCREAMING_SNAKE_CASE_ )
lowercase__ : Dict = 2
while 2 * eucl... | 214 |
import warnings
from ...utils import logging
from .image_processing_dpt import DPTImageProcessor
snake_case_ = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE__ (__snake_case ):
def __init__( self , *a , **a):
warnings.warn(
'The class D... | 214 | 1 |
'''simple docstring'''
def UpperCamelCase( UpperCAmelCase_ ):
if len(UpperCAmelCase_ ) <= 1:
return lst
UpperCAmelCase : List[str] = 1
while i < len(UpperCAmelCase_ ):
if lst[i - 1] <= lst[i]:
i += 1
else:
UpperCAmelCase : Optional[int] = lst... | 360 |
'''simple docstring'''
# Lint as: python3
import itertools
import os
import re
lowercase__ = re.compile(r"([A-Z]+)([A-Z][a-z])")
lowercase__ = re.compile(r"([a-z\d])([A-Z])")
lowercase__ = re.compile(r"(?<!_)_(?!_)")
lowercase__ = re.compile(r"(_{2... | 280 | 0 |
from collections.abc import Callable
class _snake_case :
def __init__( self , _a = None ):
__magic_name__ : int = []
# Stores indexes of each item for supporting updates and deletion.
__magic_name__ : List[str] = {}
# Stores current size of heap.
... | 281 |
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
SCREAMING_SNAKE_CASE_ = logging.get_logg... | 296 | 0 |
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 ( _lowerCAmelCase ):
A ... | 82 |
import argparse
import re
import torch
from CLAP import create_model
from transformers import AutoFeatureExtractor, ClapConfig, ClapModel
a : Optional[int] = {
'text_branch': 'text_model',
'audio_branch': 'audio_model.audio_encoder',
'attn': 'attention.self',
'se... | 82 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCAmelCase : Optional[int] = {
"configuration_clap": [
"CLAP_PRETRAINED_MODEL_ARCHIVE_LIST",
"ClapAudioConfig",
"ClapConfig",
"ClapTextCo... | 169 |
import inspect
import jax
import jax.lax as lax
import jax.numpy as jnp
from ..utils import add_start_docstrings
from ..utils.logging import get_logger
_lowerCAmelCase : int = get_logger(__name__)
_lowerCAmelCase : Any = r"\n Args:\n input_ids (`jnp.ndarray` of shape... | 169 | 1 |
'''simple docstring'''
import json
import os
import unittest
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class _lowercase ( _l... | 354 |
'''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 DDIMSc... | 129 | 0 |
"""simple docstring"""
def UpperCAmelCase__ ( ):
"""simple docstring"""
return 1
def UpperCAmelCase__ ( _UpperCAmelCase ):
"""simple docstring"""
return 0 if x < 0 else two_pence(x - 2 ) + one_pence()
def UpperCAmelCase__ ( _Uppe... | 286 |
"""simple docstring"""
def UpperCAmelCase__ ( _UpperCAmelCase ):
"""simple docstring"""
if isinstance(_UpperCAmelCase , _UpperCAmelCase ):
raise TypeError('\'float\' object cannot be interpreted as an integer' )
if isinstance(_UpperCAmelCase , _UpperCAmelCas... | 286 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_SCREAMING_SNAKE_CASE : Dict = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE : Dict ... | 157 |
"""simple docstring"""
# 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 wo... | 157 | 1 |
'''simple docstring'''
class a_ :
def __init__( self , snake_case_ ):
_lowerCAmelCase : Tuple = set_counts
_lowerCAmelCase : Union[str, Any] = max(_A )
_lowerCAmelCase : List[str] = len(_A )
... | 309 |
import argparse
import os
import gluonnlp as nlp
import mxnet as mx
import numpy as np
import torch
from gluonnlp.base import get_home_dir
from gluonnlp.model.bert import BERTEncoder
from gluonnlp.model.utils import _load_vocab
from gluonnlp.vocab import Vocab
from packaging import version
from torch import nn
... | 280 | 0 |
"""simple docstring"""
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, req... | 215 |
"""simple docstring"""
from __future__ import annotations
class __A :
"""simple docstring"""
def __init__( self , __A = 0 ) -> Dict:
a =key
def SCREAMING_SNAKE_CASE ( self , __A , __A ) -> list[str... | 215 | 1 |
import argparse
from argparse import Namespace
import torch
from torch import nn
from transformers import XGLMConfig, XGLMForCausalLM
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
_lowerCAmelCase = [
"""decoder.version""",
"""decoder.output_proj... | 82 |
import gc
import unittest
import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
ClapTextConfig,
ClapTextModelWithProjection,
RobertaTokenizer,
SpeechTaHifiGan,
SpeechTaHifiGanConfig,
)
from diffusers import (
AudioLDMPipeline,
AutoencoderKL,
DDI... | 82 | 1 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import EsmConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin,... | 55 |
'''simple docstring'''
def a_ ( lowerCamelCase : float ):
return 10 - x * x
def a_ ( lowerCamelCase : float , lowerCamelCase : float ):
# Bolzano theory in order to find if there is a root between a and b
if equation(lowerCamelCase ... | 55 | 1 |
"""simple docstring"""
import argparse
import torch
from transformers import (
UniSpeechSatConfig,
UniSpeechSatForAudioFrameClassification,
UniSpeechSatForSequenceClassification,
UniSpeechSatForXVector,
WavaVecaFeatureExtractor,
logging,
)
logging.set_verbosity_i... | 45 |
import gc
import math
import unittest
import torch
from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMixin, UNetTesterMixin
__sn... | 129 | 0 |
"""simple docstring"""
def lowerCAmelCase (__UpperCamelCase : float , __UpperCamelCase : float ):
"""simple docstring"""
if mass < 0:
raise ValueError('''The mass of a body cannot be negative''' )
return 0.5 * mass * abs(__a ) * abs(__a )
if __name__ == "__main__":
... | 365 | """simple docstring"""
import os
from pathlib import Path
def lowerCAmelCase ():
"""simple docstring"""
from torch.utils.cpp_extension import load
__UpperCamelCase =Path(__UpperCamelCase ).resolve().parent.parent.parent / '''kernels''' / '''deformable_detr'''
__Uppe... | 85 | 0 |
import functools
import gc
import inspect
import torch
from .imports import is_npu_available, is_xpu_available
def _UpperCamelCase ( *snake_case__ ) -> Optional[int]:
if not isinstance(snake_case__, snake_case__ ):
__UpperCAmelCase : Tuple = ... | 157 | import numpy as np
def _UpperCamelCase ( snake_case__ ) -> np.ndarray:
return 1 / (1 + np.exp(-vector ))
def _UpperCamelCase ( snake_case__ ) -> np.ndarray:
return vector * sigmoid(snake_case__ )
if __name__ == "__main__":
... | 157 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__snake_case = ... | 369 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__snake_case : List[str] = {
'configuration_timesformer': ['TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TimesformerConfig'],
}... | 58 | 0 |
'''simple docstring'''
from math import factorial
def snake_case_ ( lowerCAmelCase_ = 20 )-> int:
'''simple docstring'''
_UpperCAmelCase : List[Any] = 2 * n # middle entry of odd rows starting at row 3 is the solution for n = 1,
# 2, 3,...
_Up... | 215 |
'''simple docstring'''
from __future__ import annotations
import queue
class lowercase :
"""simple docstring"""
def __init__( self ,a_ ) -> str:
_UpperCAmelCase : Optional[Any] = data
_UpperCAmelCase : Optional[int] = None
... | 215 | 1 |
import os
import sys
import warnings
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import xopen
from ..table impo... | 366 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__a = {
'configuration_bloom': ['BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BloomConfig', 'BloomOnnxConfig'],
}
try:
if not is_tokenizers_available():
... | 235 | 0 |
'''simple docstring'''
import numpy
class snake_case :
"""simple docstring"""
def __init__( self , UpperCamelCase , UpperCamelCase ):
"""simple docstring"""
lowerCamelCase_ = input_array
# Random initial weights are assig... | 55 |
'''simple docstring'''
a_ : str = """
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
"""
a_ : Any... | 55 | 1 |
"""simple docstring"""
def _A ( SCREAMING_SNAKE_CASE : int ):
"""simple docstring"""
if not isinstance(__a , __a ) or number < 0:
raise ValueError("Input must be a non-negative integer" )
a__ : Any =0
while number:
# This w... | 350 |
import tempfile
import torch
from diffusers import PNDMScheduler
from .test_schedulers import SchedulerCommonTest
class __lowerCAmelCase ( UpperCamelCase__):
_lowercase : Any = (PNDMScheduler,)
_lowercase : str = (("""num_infe... | 148 | 0 |
import argparse
from tax import checkpoints
from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM
def A_ ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
SCREAMING_SNAKE_CASE_: int = AutoConfig.from_pretrained(_UpperCAmelCase )
SCREAMING_SNAKE_CASE... | 13 |
'''simple docstring'''
import warnings
from functools import wraps
from typing import Callable
def UpperCamelCase_( snake_case : Callable ):
'''simple docstring'''
@wraps(snake_case )
def _inner_fn(*snake_case : Optional[int] , **snak... | 85 | 0 |
'''simple docstring'''
import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image
from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp... | 359 | '''simple docstring'''
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorForm... | 106 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__UpperCamelCase = {
'''configuration_groupvit''': [
'''GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''Grou... | 113 |
'''simple docstring'''
import argparse
import shlex
import runhouse as rh
if __name__ == "__main__":
# Refer to https://runhouse-docs.readthedocs-hosted.com/en/latest/api/python/cluster.html#hardware-setup for cloud access
# setup instructions, if using on-demand hardware
# If user passes -... | 58 | 0 |
def __lowerCamelCase ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
snake_case_ = [False] * len(UpperCamelCase__ )
snake_case_ = []
queue.append(UpperC... | 200 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_UpperCAmelCase : Tuple = {
"""configuration_xlm_roberta_xl""": [
"""XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""XLMRobertaXLConfig""",
"""XLMRob... | 200 | 1 |
def a__ ( _UpperCamelCase : float ):
return 10 - x * x
def a__ ( _UpperCamelCase : float ,_UpperCamelCase : float ):
if equation(__a ) * equation(__a ) >= 0:
raise ValueError('''Wrong space!''' )
__lowerCamelCase ... | 330 |
a__ = '''0.18.2'''
from .configuration_utils import ConfigMixin
from .utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_inflect_available,
is_invisible_watermark_available,
is_k_diffusion_available,
is_k_diffusion_version,
is_librosa_available,
is_n... | 235 | 0 |
"""simple docstring"""
import argparse
import os
import jax as jnp
import numpy as onp
import torch
import torch.nn as nn
from music_spectrogram_diffusion import inference
from tax import checkpoints
from diffusers import DDPMScheduler, OnnxRuntimeModel, SpectrogramDiffusionPipeline
from diffusers.pipelines.spect... | 76 |
"""simple docstring"""
import math
from typing import Callable, List, Optional, Union
import numpy as np
import PIL
import torch
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion.pi... | 76 | 1 |
from collections.abc import Callable
import numpy as np
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
lowerCamelCase_ = int(np.ceil((x_end - xa) / step_size ) )
lowerCamelCase_ = np.zeros((n + 1,) )
... | 19 |
"""simple docstring"""
import inspect
import unittest
from typing import List
import numpy as np
from transformers import EfficientFormerConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_availa... | 148 | 0 |
"""simple docstring"""
import json
import os
import unittest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@req... | 356 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils impor... | 66 | 0 |
'''simple docstring'''
import os
import tempfile
import unittest
from transformers import DistilBertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_co... | 28 |
"""simple docstring"""
import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union
import flax
import jax.numpy as jnp
from ..utils import BaseOutput
__UpperCamelCase : Optional[Any] = '''scheduler_conf... | 106 | 0 |
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert_fast import BertTokenizerFast
from .tokenization_dpr import DPRContextEncoderTokenizer... | 371 |
"""simple docstring"""
import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor
def _lowerCAmelCase ( lowerCAmelCase ):
... | 248 | 0 |
'''simple docstring'''
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, DDIMScheduler... | 200 |
'''simple docstring'''
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class lowercase__ ( _snake_case ... | 200 | 1 |
'''simple docstring'''
import math
from enum import Enum
from typing import Optional, Union
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from .utils import logging
lowerCAmelCase : Any = logging.get_logger(__name__)
class UpperCame... | 25 |
'''simple docstring'''
from math import isqrt
def lowercase (_A ):
"""simple docstring"""
return all(number % divisor != 0 for divisor in range(2 , isqrt(_A ) + 1 ) )
def lowercase (_A = 1_0**6 ):
... | 25 | 1 |
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import MaMaaaTokenizer, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
)
from tra... | 76 |
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_... | 76 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_A = {
'''configuration_roc_bert''': ['''ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RoCBertConfig'''],
'''tokenization_roc_bert''': ['''RoCBertTokenizer''... | 261 |
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert import BertTokenizer
_A = logging.get_logger(__name__)
_A = {'''vocab_fil... | 261 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowercase : str = {
"configuration_xlm_roberta_xl": [
"XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP",
"XLMRoberta... | 93 |
"""simple docstring"""
import math
class lowerCamelCase :
'''simple docstring'''
def lowerCAmelCase_ ( self: Tuple , snake_case: list[list[float]] , snake_case: list[int] ) -> int:
snake_case_ :Any = 0.0
sn... | 66 | 0 |
"""simple docstring"""
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
ConditionalDetrConfig,
ConditionalDetrForObjectDetection,
Conditional... | 364 | """simple docstring"""
import os
import random
import sys
from . import cryptomath_module as cryptomath
from . import rabin_miller
SCREAMING_SNAKE_CASE__:List[str] = 3
def _lowerCamelCase( a ):
print("Generating primitive root of p" )
while True:
__a = ... | 268 | 0 |
"""simple docstring"""
import re
from filelock import FileLock
try:
import nltk
lowerCAmelCase__ : Optional[int] = True
except (ImportError, ModuleNotFoundError):
lowerCAmelCase__ : int = False
if NLTK_AVAILABLE:
with FileLock('.lock') as lock:
nltk... | 98 |
import math
import time
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
import torch_xla.debug.metrics as met
class A__(a_ ):
"""sim... | 248 | 0 |
from functools import lru_cache
@lru_cache
def lowerCamelCase__ ( A__ : Dict ):
'''simple docstring'''
if num < 0:
raise ValueError("""Number should not be negative.""" )
return 1 if num in (0, 1) else num * factorial(num - 1 )
if __name__ == "__m... | 366 |
def lowerCamelCase__ ( A__ : list ):
'''simple docstring'''
for i in range(len(A__ ) - 1 , 0 , -1 ):
__lowerCamelCase = False
for j in range(A__ , 0 , -1 ):
if unsorted[j] < unsorted[j -... | 29 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
UpperCAmelCase__ : List[str] = {
'configuration_mega': ['MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MegaConfig', 'MegaOnn... | 25 |
"""simple docstring"""
UpperCAmelCase__ : Any = '\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transform... | 25 | 1 |
import numpy as np
from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch
from .test_feature_extraction_common import FeatureExtractionSavingTestMixin
class _a ( _lowerCAmelCase ):
# to overwrite at feature extractactor spe... | 82 |
from __future__ import annotations
def lowerCAmelCase_ (lowerCAmelCase__: list[int | float] , lowerCAmelCase__: int , lowerCAmelCase__: int ):
"""simple docstring"""
if len(lowerCAmelCase__ ) == 0:
raise ValueError("""fin... | 82 | 1 |
"""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 snake_case__ ( unit... | 261 | """simple docstring"""
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
SCREAMING_SNAKE_CASE__:int = logging.get_logger(__name__)
class snake_case__ ( snake_case_ ):
_snake_case : O... | 261 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase : Union[str, Any] = logging.get_logger(__name__)
lowerCAmelCase : Any = {
"""MIT/ast-finetuned-audioset-10-10-0.4593""": (
""... | 25 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .... | 25 | 1 |
def _snake_case( SCREAMING_SNAKE_CASE__ : int = 1000 ) -> int:
'''simple docstring'''
A__ = -1
A__ = 0
for a in range(1 , n // 3 ):
# Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c
... | 7 |
"""simple docstring"""
import copy
import os
import cva
import numpy as np
from matplotlib import pyplot as plt
class UpperCamelCase_ :
def __init__( self : str ) -> Dict:
UpperCAmelCase_ : List[Any] = ""
UpperCAmelCase_ : int = ""... | 268 | 0 |
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 ... impor... | 344 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase_ = logging.get_logger(__name__)
UpperCamelCase_ = {
"s-JoL/Open-Llama-V1": "https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json",
}
class a_ ( _snake_case ):
... | 344 | 1 |
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
_lowerCAmelCase = """"""
for ch in key:
if ch == " " or ch not in key_no_dups and ch.isalpha():
key_no_dups += ch
return key_no_dups
def _UpperCAmelCase ( snake_case ):
... | 82 |
import contextlib
import csv
import json
import os
import sqlitea
import tarfile
import textwrap
import zipfile
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
import datasets
import datasets.config
@pytest.fixture(scope='session' )
def lowercase__ ( ... | 29 | 0 |
"""simple docstring"""
import os
import jsonlines
import numpy as np
from tqdm import tqdm
a : str = 2048
a : Union[str, Any] = 4096
a : Tuple = 42
a : List[Any] = os.environ.pop("""PROCESS_TRAIN""", """false""")
a : List[Any] =... | 150 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a : List[str] = {
"""configuration_xlm_roberta_xl""": [
"""XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"... | 150 | 1 |
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
return str(snake_case ) == str(snake_case )[::-1]
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
return int(snake_case ) + int(str(snake_case )[::-1] )
def _UpperCAmelCase ... | 82 |
from math import isqrt, loga
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
_lowerCAmelCase = [True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j in range(i**2 , snake_case , sna... | 82 | 1 |
"""simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConformerConfig,
WavaVecaConformerForCTC,
WavaVecaConformerForPreTraining,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaP... | 312 | """simple docstring"""
from ..utils import DummyObject, requires_backends
class UpperCamelCase ( metaclass=lowerCAmelCase__ ):
SCREAMING_SNAKE_CASE_ = ["keras_nlp"]
def __init__( self, *lowerCAmelCase__, **lowerCAmelCase__) -> int:
requires... | 312 | 1 |
"""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, ini... | 25 |
"""simple docstring"""
from typing import Dict, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import flip_channel_order, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
... | 25 | 1 |
"""simple docstring"""
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A : Optional[Any] = logging.get_logger(__name__)
A : Tuple = {
"BridgeTower/bridgetower-base"... | 355 |
"""simple docstring"""
import os
from pathlib import Path
import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader
from transformers import AutoTokenizer
from transformers.models... | 259 | 0 |
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 __UpperCAmelCase ( lowerCamelCase__ ):
UpperCamelCase = field(defa... | 336 |
def a__ ( UpperCAmelCase : int ) -> int:
UpperCAmelCase : list[list[int]] = [[0 for _ in range(UpperCAmelCase )] for _ in range(m + 1 )]
for i in range(m + 1 ):
UpperCAmelCase : Optional[Any] = 1
for n in range(m + 1 ):
for k in range(1 , Upp... | 336 | 1 |
from pickle import UnpicklingError
import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict
from ..utils import logging
__SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
def UpperCAmelCase ( ... | 358 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indic... | 256 | 0 |
"""simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
HubertConfig,
HubertForCTC,
HubertModel,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
logging,
)
... | 150 | """simple docstring"""
import itertools
import string
from collections.abc import Generator, Iterable
def lowerCAmelCase__ ( _UpperCamelCase : Iterable[str] , _UpperCamelCase : int ) -> Generator[tuple[str, ...], None, None]:
"""simple docstring"""
... | 150 | 1 |
import argparse
import json
import pickle
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils import loggin... | 131 |
def __snake_case ( _UpperCAmelCase , _UpperCAmelCase ):
def get_matched_characters(_UpperCAmelCase , _UpperCAmelCase ) -> str:
__a = []
__a = min(len(_stra ) , len(_stra ) ) // 2
for i, l in enumerate(_stra ):
... | 131 | 1 |
from typing import List
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase : str = logging.get_logger(__name__)
_UpperCAmelCase : str = {
"snap-research/efficientformer-l1-300": (
"https://huggingface.co/snap-research/efficientformer-l1-300/reso... | 236 |
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments
import transformers
from transformers import (
AutoConfig,
AutoModelForSeqaSeqL... | 327 | 0 |
import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union
import numpy as np
def __UpperCamelCase ( _A : bytes , _A : int ) ->np.array:
"""simple docstring"""
lowerCamelCase_ =f'{sampling_rate}'
lowerCame... | 49 |
from __future__ import annotations
import numpy as np
from numpy import floataa
from numpy.typing import NDArray
def __UpperCamelCase ( _A : NDArray[floataa] , _A : NDArray[floataa] , _A : list[int] , _A : int , ) ->list[float]:
... | 49 | 1 |
import math
def A_ ( a = 1_0_0 ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Dict = sum(i * i for i in range(1 , n + 1 ) )
SCREAMING_SNAKE_CASE_ : List[str] = int(math.pow(sum(range(1 , n + 1 ) ) , 2 ) ... | 253 |
def _A ( SCREAMING_SNAKE_CASE__ : Union[str, Any] , SCREAMING_SNAKE_CASE__ : List[str] , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : Optional[Any] ):
# Return True if there is node that has not iterated.
UpperCamelCase :Tuple = [False] * len(SCRE... | 259 | 0 |
import tensorflow as tf
from ...tf_utils import shape_list
class snake_case__ (tf.keras.layers.Layer ):
"""simple docstring"""
def __init__( self , __lowercase , __lowercase , __lowercase , __lowercase , __lower... | 368 |
from __future__ import annotations
def lowerCAmelCase_ ( _lowercase : float , _lowercase : float , _lowercase : float , ) -> tuple[str, float]:
"""simple docstring"""
if (stress, tangential_force, area).count(0)... | 266 | 0 |
from __future__ import annotations
import time
import numpy as np
__A = [8, 5, 9, 7]
__A = [
[2, 0, 1, 1],
[0, 1, 2, 1],
[4, 0, 0, 3],
[0, 2, 1, 0],
[1, 0, 3, 0],
]
__A = [
[3, 2, 1, 4],
[0, 2, 5, 2],
[5, 1, 0, 5],
[1, 5, 3... | 90 | """simple docstring"""
def lowercase ( a__ : Union[str, Any] ) -> Optional[Any]:
_UpperCamelCase = len(a__ )
while cur > 1:
# Find the maximum number in arr
_UpperCamelCase = arr.index(max(arr[0:cur] ) )
# Reverse from 0 t... | 256 | 0 |
from __future__ import annotations
import math
from collections import Counter
from string import ascii_lowercase
def lowercase__ ( __snake_case : str ):
UpperCAmelCase_ : Optional[int] = analyze_text(__snake_case )
UpperCAmelCase_ : Tupl... | 361 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__UpperCAmelCase = {
'configuration_roc_bert': ['ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RoCBertConfig'],
'tokenization_roc_ber... | 145 | 0 |
def lowerCamelCase_ ( _a , _a = 0 ):
"""simple docstring"""
lowerCAmelCase__ : Union[str, Any] = length or len(_a )
lowerCAmelCase__ : List[str] = False
for i in range(length - 1 ):
if list_data[i] > list_data[i... | 131 |
import collections
import inspect
import unittest
from transformers import SwinvaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common impor... | 131 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__lowerCamelCase : Dict = {
'''configuration_nllb_moe''': [
'''NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''NllbMoeConfig''',
]
}
try:
i... | 354 | from math import isqrt
def __SCREAMING_SNAKE_CASE ( __UpperCamelCase : int ) -> list[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = [True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
... | 204 | 0 |
from argparse import ArgumentParser
from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand
__snake_case :Optional[int] = logging.get_logger(__name__) # pylint: disable=invalid-name
def __s... | 49 |
from collections import defaultdict
def __snake_case ( _UpperCAmelCase , _UpperCAmelCase ):
__a = first_str.lower().strip()
__a = second_str.lower().strip()
# Remove whitespace
__a = first_str.replace(''' ''' , '''''' )
__a ... | 49 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A = logging.get_logger(__name__)
A = {
'''uw-madison/mra-base-512-4''': '''https://huggingface.co/uw-madison/mra-base-512-4/resolve/main/config.json''',
}
... | 188 |
"""simple docstring"""
def __A ( a_ :int = 1_00_00_00) -> int:
__a : Tuple = [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 , a_):
... | 188 | 1 |
'''simple docstring'''
import os
from argparse import ArgumentParser, Namespace
from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLIComma... | 55 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
i... | 266 | 0 |
"""simple docstring"""
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 no... | 366 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_lowercase : Tuple ... | 86 | 0 |
from __future__ import annotations
from math import pi, sqrt
def _lowercase ( UpperCamelCase_ , UpperCamelCase_ ) -> Optional[Any]:
'''simple docstring'''
if inductance <= 0:
raise ValueError('Inductance cannot be 0 or negative' )
elif capacitance <= 0:
... | 176 | '''simple docstring'''
from binascii import hexlify
from hashlib import shaaaa
from os import urandom
# RFC 3526 - More Modular Exponential (MODP) Diffie-Hellman groups for
# Internet Key Exchange (IKE) https://tools.ietf.org/html/rfc3526
__a = {
# 1536-bit
5: {
'prime': int(
... | 145 | 0 |
"""simple docstring"""
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.conversat... | 74 |
"""simple docstring"""
import qiskit
def __SCREAMING_SNAKE_CASE ( A_ = 2 ):
lowerCAmelCase__ : int = qubits
# Using Aer's simulator
lowerCAmelCase__ : str = qiskit.Aer.get_backend('''aer_simulator''' )
# Creating a Quantum Circuit acting on the q register
low... | 74 | 1 |
'''simple docstring'''
def UpperCAmelCase_ ( __lowercase : int ) -> list:
'''simple docstring'''
_UpperCAmelCase = int(__lowercase )
if n_element < 1:
_UpperCAmelCase = ValueError("a should be a positive number" ... | 22 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowerCamelCase : str = {
"configuration_nezha": ["NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP", "NezhaConfig"],
}
try:
if not is_torch_available()... | 204 | 0 |
import logging
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
BERT_START_DOCSTRING,... | 352 |
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_... | 238 | 0 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import... | 188 |
from collections import UserDict
from typing import Union
import numpy as np
import requests
from ..utils import (
add_end_docstrings,
logging,
)
from .audio_classification import ffmpeg_read
from .base import PIPELINE_INIT_ARGS, Pipeline
lowerCamelCase = logging.get_logger(__name__)
... | 188 | 1 |
'''simple docstring'''
def __lowerCamelCase ( A__ , A__ ) -> list[str]:
"""simple docstring"""
return [sentence[i : i + ngram_size] for i in range(len(A__ ) - ngram_size + 1 )]
if __name__ == "__main__":
from doctest import testmod
testmod()
... | 249 |
'''simple docstring'''
def __lowerCamelCase ( A__ , A__ , A__ , A__ , A__ , A__ ) -> int:
"""simple docstring"""
if index == r:
for j in range(A__ ):
print(data[j] , end=' ' )
print(' ' )
r... | 249 | 1 |
"""simple docstring"""
def _A ( UpperCamelCase_ : Optional[int]) -> Any:
'''simple docstring'''
if n == 1 or not isinstance(_UpperCamelCase, _UpperCamelCase):
return 0
elif n == 2:
return 1
else:
__lowercase = [0, 1]
for i in range(2, n + 1... | 17 |
"""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
lowerCamelCase__ = logging.get_logger(__name__)
lowerCamelCase__ ... | 86 | 0 |
import json
import os
import tempfile
from transformers.testing_utils import check_json_file_has_correct_format
class a_ :
'''simple docstring'''
UpperCAmelCase_ = None
def __snake_case ( self : Dict):
'''simple docstring'''
lowerCAmelCase__ ... | 119 | from math import log
from scipy.constants import Boltzmann, physical_constants
lowerCAmelCase__ = 300 # TEMPERATURE (unit = K)
def __lowerCamelCase ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , ):
if donor_conc <= 0:
raise ValueError('Dono... | 119 | 1 |
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