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 |
|---|---|---|---|---|
from math import pow, sqrt
def A_ ( *snake_case : float ) -> bool:
'''simple docstring'''
__UpperCamelCase = len(snake_case ) > 0 and all(value > 0.0 for value in values )
return result
def A_ ( snake_case : ... | 328 |
from __future__ import annotations
from collections import deque
class SCREAMING_SNAKE_CASE__ :
"""simple docstring"""
def __init__( self , SCREAMING_SNAKE_CASE_ )-> int:
'''simple docstring'''
__UpperCamelCase = []
... | 328 | 1 |
from __future__ import annotations
def _lowerCamelCase( lowercase__ , lowercase__ ) -> List[str]:
'''simple docstring'''
print(F'Vertex\tShortest Distance from vertex {src}' )
for i, d in enumerate(lowercase__ ):
print(F'{i}\t\t{d}' )
def _lowerC... | 304 |
def _lowerCamelCase( lowercase__ = 1_0_0_0 ) -> int:
'''simple docstring'''
__lowercase= 2**power
__lowercase= str(lowercase__ )
__lowercase= list(lowercase__ )
__lowercase= 0
for i in list_num:
sum_of_num += int(lowercase__ )
return sum_o... | 304 | 1 |
"""simple docstring"""
import argparse
import logging
import pickle
from collections import Counter
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", level=logging.INFO
)
UpperCAmelCase_ : Dict = logging.getLogger(__name__)... | 91 |
"""simple docstring"""
from __future__ import annotations
class lowerCAmelCase__ :
'''simple docstring'''
def __init__( self : Any , lowercase_ : int = 0):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ : List[Any] ... | 91 | 1 |
"""simple docstring"""
import unittest
from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import Toke... | 316 |
"""simple docstring"""
# Note: if you intend to run this script make sure you look under scripts/fsmt/
# to locate the appropriate script to do the work correctly. There is a set of scripts to:
# - download and prepare data and run the conversion script
# - perform eval to get the best hparam into the config
# -... | 316 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_SCREAMING_SNAKE_CASE : Tuple = {
"configuration_conditional_detr": [
"CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP"... | 85 |
import math
from datetime import datetime, timedelta
def __A ( __lowerCamelCase ) -> datetime:
a = year % 19
a = year % 4
a = year % 7
a = math.floor(year / 100 )
a = math.floor((13 + 8 * leap_... | 228 | 0 |
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 .tokeni... | 88 |
import argparse
import json
import math
import os
import time
import traceback
import zipfile
from collections import Counter
import requests
def SCREAMING_SNAKE_CASE__ ( __a , __a=None ):
snake_case_ : Optional[int] = None
if token is not None:
snake_c... | 88 | 1 |
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checko... | 257 |
import argparse
import torch
from torch import nn
from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def __lowercase ( a__ ) -> Tuple:
__SCREAMING_SNAKE_CASE = [
'encoder.version',
'decoder.version',
'model.encoder.version',
... | 257 | 1 |
"""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/... | 11 |
"""simple docstring"""
from __future__ import annotations
def lowercase ( lowerCAmelCase__ : list[int] , lowerCAmelCase__ : list[int] , lowerCAmelCase__ : list[int] , lowerCAmelCase__ : list[list[str]] , lowerCAmelCase__ : int , ) -> None:
__a ... | 11 | 1 |
import mpmath # for roots of unity
import numpy as np
class A :
def __init__(self : Union[str, Any] , __UpperCAmelCase : Any=None , __UpperCAmelCase : Optional[int]=None ) -> Dict:
"""simple docstring"""
UpperCAm... | 65 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
fro... | 2 | 0 |
'''simple docstring'''
from __future__ import annotations
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import is_tf_available, is_vision_available
from ...test_modeling_tf_common import... | 96 |
'''simple docstring'''
import re
import string
from collections import Counter
import sacrebleu
import sacremoses
from packaging import version
import datasets
lowerCAmelCase: Dict = '\n@inproceedings{xu-etal-2016-optimizing,\n title = {Optimizing Statistical Machine Translation for Text Sim... | 96 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
lowerCamelCase_ = logging.get_logger(__name__)
class __A( __lowerCamelCase , __lowerCamelCase ):
"""simple ... | 244 |
import unittest
from transformers import DebertaVaTokenizer, DebertaVaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
lowerCamelCase_ = get_tests_dir('''fixtures/spiece.model''... | 244 | 1 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__UpperCAmelCase : List[str] = logging.get_logger(__name__)
__UpperCAmelCase : List[Any] = {"... | 315 |
def a ( SCREAMING_SNAKE_CASE_ : str ):
"""simple docstring"""
return "".join(chr(ord(SCREAMING_SNAKE_CASE_ ) - 3_2 ) if '''a''' <= char <= '''z''' else char for char in word )
if __name__ == "__main__":
from doctest import testmod
t... | 315 | 1 |
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
'huggingface/time-series-transformer-tourism-monthly': (
'https://huggingface.co/huggingface/tim... | 11 |
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ ) -> list:
if len(SCREAMING_SNAKE_CASE_ ) <= 1:
return [tuple(SCREAMING_SNAKE_CASE_ )]
lowerCAmelCase__ : Optional[Any] = []
def generate(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
... | 212 | 0 |
def _snake_case( SCREAMING_SNAKE_CASE__ : float , SCREAMING_SNAKE_CASE__ : float , SCREAMING_SNAKE_CASE__ : float , SCREAMING_SNAKE_CASE__ : float , SCREAMING_SNAKE_CASE__ : float , ) -> float:
'''simple ... | 357 |
import warnings
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis... | 282 | 0 |
'''simple docstring'''
from urllib.parse import quote
import pytest
from datasets.utils.hub import hf_hub_url
@pytest.mark.parametrize('repo_id' , ['canonical_dataset_name', 'org-name/dataset-name'] )
@pytest.mark.parametrize('path' , ['filename.csv', 'filename with blanks.csv'] )
@pytest.mar... | 42 |
import random
from .binary_exp_mod import bin_exp_mod
def _lowerCAmelCase ( __lowerCAmelCase , __lowerCAmelCase=1000 ) -> str:
"""simple docstring"""
if n < 2:
return False
if n % 2 == 0:
return n == 2
# this means n is odd
... | 230 | 0 |
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer
lowercase__ : str = logging.get_logger(__name__)
lowercase__ : Optional[Any] = {"vocab_file": "vocab.js... | 354 |
# Author: OMKAR PATHAK, Nwachukwu Chidiebere
# Use a Python dictionary to construct the graph.
from __future__ import annotations
from pprint import pformat
from typing import Generic, TypeVar
lowercase__ : str = TypeVar("T")
class a__ ( Generic[T] ):
def __init__( self ... | 180 | 0 |
'''simple docstring'''
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
_lowerCamelCase : Optional[int] = logging.get_logger(__name__)
_lowerCamelCase : Optional[Any] = {
"ut/deta": "http... | 28 |
from __future__ import annotations
from random import choice
def __UpperCamelCase ( _A : str ) ->int:
"""simple docstring"""
return choice(_A )
def __UpperCamelCase ( _A : list[int] , _A : int ) ->int:
"""simple docstring"""
... | 154 | 0 |
'''simple docstring'''
import os
from collections.abc import Iterator
def lowerCAmelCase_ ( snake_case_ : str = "." ) -> Iterator[str]:
'''simple docstring'''
for dir_path, dir_names, filenames in os.walk(snake_case_ ):
UpperCAmelCase_ = [d for d in ... | 358 | '''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from torch import nn
from transformers import (
SpeechaTextaConfig,
SpeechaTextaForCausalLM,
SpeechaTextaTokenizer,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaF... | 106 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
A : List[str] = {
'configuration_blenderbot_small': [
'BLENDERBOT... | 305 |
A : Union[str, Any] = {0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}
A : List[Any] = {0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}
def UpperCamelCase ( __magic_name__ : dict[int, list[int]] , __magic_name__ : int , __magic_name__ ... | 305 | 1 |
'''simple docstring'''
import argparse
import collections
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def UpperCamelCase ... | 123 |
'''simple docstring'''
__lowerCAmelCase : Dict ="\n# Installazione di Transformers\n! pip install transformers datasets\n# Per installare dalla fonte invece dell'ultima versione rilasciata, commenta il comando sopra e\n# rimuovi la modalità commento al comando seguente.\n# ! pip install git+https... | 123 | 1 |
# 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/LICENSE-2.0
#
# Unless required by applic... | 11 |
def _UpperCAmelCase (UpperCamelCase__ : str , UpperCamelCase__ : bool = False ):
if not isinstance(UpperCamelCase__ , UpperCamelCase__ ):
_A : Optional[Any] = f"Expected string as input, found {type(UpperCamelCase__ )}"
... | 11 | 1 |
"""simple docstring"""
_A = '''Alexander Joslin'''
import operator as op
from .stack import Stack
def lowerCamelCase__ ( a__ : str ) -> int:
UpperCamelCase_ = {"""*""": op.mul, """/""": op.truediv, """+""": op.add, """-""": op.sub}
UpperCamelCase_... | 368 |
from __future__ import annotations
from typing import Any
class lowercase_ :
def __init__( self , __UpperCamelCase = 6 ):
"""simple docstring"""
UpperCamelCase_ = None
UpperCamelCase_ = None
self.create_linked_list(__UpperCamelCa... | 261 | 0 |
"""simple docstring"""
import time
from contextlib import contextmanager
from pathlib import Path
import pytest
import requests
from huggingface_hub.hf_api import HfApi, HfFolder
__lowerCamelCase = "__DUMMY_TRANSFORMERS_USER__"
__lowerCamelCase = "Dummy User"
__lowerCamelCas... | 221 | """simple docstring"""
import sacrebleu as scb
from packaging import version
from sacrebleu import CHRF
import datasets
__lowerCamelCase = "\\n@inproceedings{popovic-2015-chrf,\n title = \"chr{F}: character n-gram {F}-score for automatic {MT} evaluation\",\n author = \"Popovi{\'c}, M... | 221 | 1 |
import json
import os
import unittest
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ , unittest.TestC... | 171 |
import argparse
import os
import torch
from transformers import (
XLNetConfig,
XLNetForQuestionAnswering,
XLNetForSequenceClassification,
XLNetLMHeadModel,
load_tf_weights_in_xlnet,
)
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
lowercase : Optional[int] = ... | 171 | 1 |
import random
from typing import Any
def lowerCamelCase_ ( _a : list ):
'''simple docstring'''
for _ in range(len(_a ) ):
UpperCAmelCase_ : Tuple = random.randint(0 , len(_a ) - 1 )
UpperCAmelCase_ : List[Any] = random.randint(0 ... | 345 |
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel
from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
clas... | 345 | 1 |
'''simple docstring'''
import argparse
import logging
import os
import re
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
DataCollatorForLanguageModeling,
PushToHubCallback,
TFAutoModelForMaskedLM,
create_optimizer,
)
lowerCAmelCase :str = log... | 275 |
'''simple docstring'''
def lowerCamelCase ( lowerCAmelCase : str ):
"""simple docstring"""
if not all(x.isalpha() for x in string ):
raise ValueError('String must only contain alphabetic characters.' )
__magic_name__ : int = sorted(string.lower() )
return len(l... | 275 | 1 |
'''simple docstring'''
__lowerCamelCase = {}
def UpperCAmelCase__ ( UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__ ) -> int:
# if we are absent twice, or late 3 consecutive days,
# no further prize strings are possible
if late == 3 or absent == 2:
... | 162 |
'''simple docstring'''
import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu... | 162 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_en... | 353 |
def _a ( SCREAMING_SNAKE_CASE = 10_00 ):
"""simple docstring"""
return sum(e for e in range(3 , SCREAMING_SNAKE_CASE ) if e % 3 == 0 or e % 5 == 0 )
if __name__ == "__main__":
print(f"""{solution() = }""")
| 93 | 0 |
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.models import Sequential
if __name__ == "__main__":
UpperCamelCase = pd.read_csv('''sample_data.csv''', header=None)
UpperCamelCas... | 87 |
'''simple docstring'''
import json
import os
import torch
from diffusers import UNetaDModel
os.makedirs('hub/hopper-medium-v2/unet/hor32', exist_ok=True)
os.makedirs('hub/hopper-medium-v2/unet/hor128', exist_ok=True)
os.makedirs('hub/hopper-medium-v2/value_function', exist_ok=True)
... | 163 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__magic_name__: List[str] = {
"configuration_mobilebert": [
"MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_M... | 138 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__magic_name__: List[Any] = {
"configuration_conditional_detr": [
"CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP",
"ConditionalDetrConfi... | 138 | 1 |
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torc... | 175 | from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def __lowercase ( ):
UpperCamelCase_ : Optional[Any] = HfArgumentParser(lowerCamelCase )
UpperCamelCase_ : Tuple = parser.parse_args_into_dataclasses()[0]
UpperCamelCase_ : ... | 175 | 1 |
from functools import lru_cache
def UpperCamelCase__ ( a_ ) -> set:
"""simple docstring"""
__A = 2
__A = set()
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.add(__lowerCAmelCa... | 350 |
# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by app... | 124 | 0 |
import math
import qiskit
def __a ( SCREAMING_SNAKE_CASE = 1 , SCREAMING_SNAKE_CASE = 1 , SCREAMING_SNAKE_CASE = 1 ) -> qiskit.result.counts.Counts:
'''simple docstring'''
if (
isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CAS... | 333 |
def __a ( SCREAMING_SNAKE_CASE ) -> set:
'''simple docstring'''
__UpperCAmelCase = set()
# edges = list of graph's edges
__UpperCAmelCase = get_edges(SCREAMING_SNAKE_CASE )
# While there are still elements in edges list, take an arbitrary edg... | 333 | 1 |
'''simple docstring'''
from __future__ import annotations
import os
import tempfile
import unittest
from transformers import ConvBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFM... | 355 |
'''simple docstring'''
def UpperCAmelCase_ (__a : int = 1_0**1_2 ):
"""simple docstring"""
_a : List[str] = 1
_a : Optional[int] = 0
_a : Any = 1
_a : List[str] = 1
while numerator <= 2 * min_total - 1:
... | 5 | 0 |
from typing import List, Optional
import numpy as np
from ...processing_utils import ProcessorMixin
from ...utils import to_numpy
class __a ( snake_case_ ):
__lowercase : Union[str, Any] = """EncodecFeatureExtractor"""
__lowercase : int = ("""T5Tokenizer... | 196 | """simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy
SCREAMING_SNAKE_CASE__:List[Any] = log... | 261 | 0 |
"""simple docstring"""
import os
from bleurt import score # From: git+https://github.com/google-research/bleurt.git
import datasets
__A : Optional[int] = datasets.logging.get_logger(__name__)
__A : Tuple = '''\
@inproceedings{bleurt,
title={BLEURT: Learning Robust Met... | 27 |
"""simple docstring"""
from typing import Any
class lowerCamelCase :
def __init__( self , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Optional[int] = data
UpperCamelCase : Optional[Any] = None
def __repr__( self ):... | 27 | 1 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
expand_dims,
flatten_dict,
is_flax_available,
is_tf_available,
is_torch_available,
reshape,
squeeze,
transpose,
)
... | 171 |
"""simple docstring"""
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
flip_channel_order,
get_resize_output_image_size,
rescale,
resize,
... | 171 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = {
"roberta-base": "https://h... | 332 |
'''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 # pr... | 332 | 1 |
# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile
from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version import Version
SCREAMING_SNAKE_CASE... | 15 |
import os
from itertools import chain
from random import randrange, shuffle
import pytest
from .sola import PokerHand
lowerCAmelCase_ = (
'''4S 3H 2C 7S 5H''',
'''9D 8H 2C 6S 7H''',
'''2D 6D 9D TH 7D''',
'''TC 8C 2S JH 6C''',
'''JH 8S TH AH QH''',
'''TS KS 5S 9S AC''',
'''KD 6... | 279 | 0 |
import argparse
import requests
import torch
from PIL import Image
from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor
def SCREAMING_SNAKE_CASE ( lowercase_ ) -> str:
"""simple docstring"""
if "cls_token" in name:
A__ = name... | 350 |
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 | 0 |
import sys
def snake_case__ ( SCREAMING_SNAKE_CASE_ : List[Any] ):
'''simple docstring'''
lowercase__ : List[Any] = len(SCREAMING_SNAKE_CASE_ )
lowercase__ : Optional[int] = [[0 for x in range(SCREAMING_SNAKE_CASE_ )] for x in range(SCREAMING_SNA... | 214 |
import logging
import re
import pytorch_quantization
import pytorch_quantization.nn as quant_nn
import torch
from pytorch_quantization import calib
from pytorch_quantization.tensor_quant import QuantDescriptor
snake_case_ = logging.getLogger(__name__)
snake_case_ = 50 # max width of layer names
snake_cas... | 214 | 1 |
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate impor... | 348 |
from dataclasses import dataclass
from typing import Dict, Optional, Union
import torch
import torch.nn.functional as F
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .attention... | 348 | 1 |
from __future__ import annotations
class a_ :
"""simple docstring"""
def __init__( self , _lowerCamelCase , _lowerCamelCase ) ->int:
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Tuple = text, pattern
SCREAMIN... | 313 |
import csv
import tweepy
# Twitter API credentials
a__ : Union[str, Any] = ''''''
a__ : List[str] = ''''''
a__ : Any = ''''''
a__ : List[str] = ''''''
def UpperCAmelCase_( a__ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE ... | 313 | 1 |
import numpy as np
def A ( _UpperCAmelCase : int , _UpperCAmelCase : Dict , _UpperCAmelCase : Optional[int] , _UpperCAmelCase : Dict , _UpperCAmelCase : Optional[Any] ) -> Optional[int]:
... | 290 |
from __future__ import annotations
import unittest
from transformers import DebertaVaConfig, 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, random_attention_mask
f... | 290 | 1 |
"""simple docstring"""
import collections
import json
import math
import os
import re
import time
from fnmatch import fnmatch
from typing import Dict
import requests
from slack_sdk import WebClient
lowerCamelCase__ = WebClient(token=os.environ["""CI_SLACK_BOT_TOKEN"""])
def __lowerCAmelCase (_... | 86 |
import unicodedata
from dataclasses import dataclass
from typing import Optional, Union
import numpy as np
from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase
def _lowercas... | 275 | 0 |
"""simple docstring"""
def UpperCAmelCase ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) -> bool:
return not any(
neighbour == 1 and colored_vertices[i] == color
for i, neighbour in enumerate(UpperCAmelCase ) )
def UpperCAmelCase ( UpperCAmelCase , Uppe... | 312 | """simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_out... | 312 | 1 |
'''simple docstring'''
import math
import tensorflow as tf
from packaging import version
def __lowerCamelCase ( A__ ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase = tf.convert_to_tensor(A__ )
UpperCamelCase = 0.5 * (1.0 + ... | 28 |
UpperCAmelCase__ = {
'''A''': '''.-''', '''B''': '''-...''', '''C''': '''-.-.''', '''D''': '''-..''', '''E''': '''.''', '''F''': '''..-.''', '''G''': '''--.''',
'''H''': '''....''', '''I''': '''..''', '''J''': '''.---''', '''K''': '''-.-''', '''L''': '''.-..''', '''M''': '''--''', '''N''': '''-.''',... | 5 | 0 |
"""simple docstring"""
import datasets
a_ = "\\n@InProceedings{conneau2018xnli,\n author = \"Conneau, Alexis\n and Rinott, Ruty\n and Lample, Guillaume\n and Williams, Adina\n and Bowman, Samuel R.\n and Schw... | 368 |
"""simple docstring"""
from collections import namedtuple
import requests
from lxml import html # type: ignore
a_ = namedtuple("covid_data", "cases deaths recovered")
def a__ ( __lowercase = "https://www.worldometers.info/coronavirus/" ) -> covid_data:
_... | 163 | 0 |
'''simple docstring'''
from json import JSONDecodeError # Workaround for requests.exceptions.JSONDecodeError
import requests
def lowerCamelCase (_SCREAMING_SNAKE_CASE : str = "isbn/0140328726" ):
__a : List[str] = olid.strip().strip('/' ) # Remove leading/trailing... | 27 |
'''simple docstring'''
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
execute_subprocess_async,
require_cpu,
require_huggingface_suite,
require_multi_gpu,
require_single_gpu,
)
from accelerate.... | 27 | 1 |
from typing import Dict, Optional
import numpy as np
import datasets
lowerCamelCase = '''
IoU is the area of overlap between the predicted segmentation and the ground truth divided by the area of union
between the predicted segmentation and the ground truth. For binary (two classes) or multi-class segmenta... | 362 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCamelCase = logging.get_logger(__name__)
lowerCamelCase = {
'''facebook/xlm-roberta-xl''': '''https://h... | 211 | 0 |
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 .tok... | 71 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from ... | 171 | 0 |
"""simple docstring"""
import unittest
import torch
from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow
__A = False
class _lowerCAmelCase ( unittest.TestCase ):
"""s... | 254 |
"""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 (
MobileViTConfig,
MobileViTForImageClassification,
MobileViTForSemanticSegmentation,
MobileViT... | 254 | 1 |
import unittest
from knapsack import greedy_knapsack as kp
class _snake_case ( unittest.TestCase ):
'''simple docstring'''
def A__ ( self: int ) -> Union[str, Any]:
UpperCAmelCase_ : Union[str, Any] = [10, 20, 30, 40, 50, 60]
UpperCAmelC... | 345 |
import functools
def lowerCamelCase__ ( __lowerCAmelCase : str , __lowerCAmelCase : str ):
"""simple docstring"""
lowerCAmelCase_ = len(__lowerCAmelCase )
lowerCAmelCase_ = len(__lowerCAmelCase )
@functools.cache
def min_distance(__lowerCAmelCase : ... | 231 | 0 |
"""simple docstring"""
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch
if is_vision_available():
from transformers import TvltImageProcessor
if is_speec... | 271 |
"""simple docstring"""
def _snake_case ( _snake_case : str ) -> str:
'''simple docstring'''
_A = ''
for ch in key:
if ch == " " or ch not in key_no_dups and ch.isalpha():
key_no_dups += ch
return key_no_dups
d... | 271 | 1 |
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, Distr... | 348 | import os
from argparse import ArgumentParser
from typing import List
import torch.utils.data
from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node
__snake_case = 4
__snake_case = 3
class __snake_case ( lowerCamelCase__ ):
pass
d... | 348 | 1 |
"""simple docstring"""
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... | 27 |
"""simple docstring"""
import gc
import unittest
from diffusers import FlaxStableDiffusionInpaintPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_util... | 27 | 1 |
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,
DDIMS... | 253 |
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,
DDIMS... | 253 | 1 |
'''simple docstring'''
def lowerCamelCase__ ( A : int , A : int ):
'''simple docstring'''
return int(input_a == input_a == 0 )
def lowerCamelCase__ ( ):
'''simple docstring'''
print('''Truth Table of NOR Gate:''' ... | 354 |
'''simple docstring'''
from __future__ import annotations
from collections import namedtuple
from dataclasses import dataclass
@dataclass
class UpperCamelCase__:
__magic_name__ : int
__magic_name__ : TreeNode | None = None
__magic_name__ : TreeNode | None = None
... | 91 | 0 |
def __snake_case ( __UpperCamelCase : list[int] ,__UpperCamelCase : list[int] ,__UpperCamelCase : int ):
"""simple docstring"""
return not any(
neighbour == 1 and colored_vertices[i] == color
for i, neighbour in enumerate(__U... | 312 |
from unittest import TestCase
from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters
def __snake_case ( ):
"""simple docstring"""
A_ = {
"repo_name": ["test_repo1", "test_repo2", "test_repo3"],
"... | 312 | 1 |
import argparse
import os
import re
import packaging.version
__lowerCAmelCase = '''examples/'''
__lowerCAmelCase = {
'''examples''': (re.compile(r'''^check_min_version\("[^"]+"\)\s*$''', re.MULTILINE), '''check_min_version("VERSION")\n'''),
'''init''': (re.compile(r'''^__versi... | 368 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__lowerCAmelCase = logging.get_logger(__name__)
__lowerCAmelCase = '... | 288 | 0 |
import warnings
from ..trainer import Trainer
from ..utils import logging
__A : Union[str, Any] = logging.get_logger(__name__)
class _SCREAMING_SNAKE_CASE ( a__):
def __init__( self , _SCREAMING_SNAKE_CASE=None , **_SCREAMING_SNAKE_CASE )-> Optional[int]:
... | 154 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A =logging.get_logger(__name__)
__A ={
'google/pegasus-large': 'https://huggingface.co/google/pegasus-large/resolve/main/config.json',
# See all PEGASUS models at https://huggingfa... | 163 | 0 |
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,
)
lowerCAmelCase_ = {
"""configuration_xlm_roberta""": [
... | 353 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = {
"""google/fnet-base""": """https://huggingface.co/google/fnet-base/resolve/main/config.json""",
"""google/fnet-large""": """https://hug... | 260 | 0 |
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class a ( lowercase__ ):
"""simple docstring"""
a : str = 'Speech2TextFeatureExtractor'
a : str = 'Speech2TextTokenizer'
def ... | 114 |
import argparse
import io
import requests
import torch
from omegaconf import OmegaConf
from diffusers import AutoencoderKL
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import (
assign_to_checkpoint,
conv_attn_to_linear,
create_vae_diffusers_config,
renew_vae_at... | 114 | 1 |
"""simple docstring"""
def lowercase ( A_ , A_ )-> List[str]:
'''simple docstring'''
if not (isinstance(_A , _A ) and isinstance(_A , _A )):
raise ValueError("longest_common_substring() takes two strings for inputs" )
a : Opti... | 353 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_dpt import DPTImageProcessor
__lowercase = logging.get_logger(__name__)
class _A ( _a ):
"""simple docstring"""
def __init__( self : L... | 226 | 0 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_poolformer import PoolFormerImageProcessor
_lowerCAmelCase :Any = logging.get_logger(__name__)
class _UpperCAmelCase ( _UpperCAmelCase ):
'''simple docstring'''
def __init__( ... | 263 |
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 | 0 |
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is... | 350 |
__A = '''ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/'''
def snake_case_(_UpperCamelCase ) -> bytes:
"""simple docstring"""
if not isinstance(_UpperCamelCase , _UpperCamelCase ):
_snake_case = F"""a bytes-like object is required, no... | 278 | 0 |
'''simple docstring'''
def UpperCAmelCase_ (__a : int ):
"""simple docstring"""
if not isinstance(__a , __a ):
raise TypeError('only integers accepted as input' )
else:
_a : int = str(abs(__a ) )
_a : Any = [list(__a ) for char in range(len(__a ) )]
... | 271 |
'''simple docstring'''
from __future__ import annotations
from random import choice
def UpperCAmelCase_ (__a : str ):
"""simple docstring"""
return choice(__a )
def UpperCAmelCase_ (__a : list[int] , __a : int ):
"""simple docstring"""
_a ... | 271 | 1 |
"""simple docstring"""
def __lowerCamelCase ( a_ : str , a_ : str ) -> str:
__SCREAMING_SNAKE_CASE :int = len(a_ )
__SCREAMING_SNAKE_CASE :int = len(a_ )
__SCREAMING_SNAKE_CASE :int = (
... | 239 |
"""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__)
lowerCam... | 239 | 1 |
'''simple docstring'''
import argparse
import os
import re
__lowercase : List[str] = 'src/transformers'
# Pattern that looks at the indentation in a line.
__lowercase : Any = re.compile(R'^(\s*)\S')
# Pattern that matches `"key":" and puts `key` in group 0.
__lowercase : str = ... | 27 |
'''simple docstring'''
#
# This a `torch.distributed` diagnostics script that checks that all GPUs in the cluster (one or
# many nodes) can talk to each other via nccl and allocate gpu memory.
#
# To run first adjust the number of processes and nodes:
#
# python -m torch.distributed.run --nproc_per_node 2 --nnode... | 27 | 1 |
import os # noqa: this is just for tests
import os as renamed_os # noqa: this is just for tests
from os import path # noqa: this is just for tests
from os import path as renamed_path # noqa: this is just for tests
from os.path import join # noqa: this is just for tests
from os.path import join as renamed_joi... | 355 |
_UpperCAmelCase = {0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}
_UpperCAmelCase = {0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}
def _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE :dict[int, list[int]] , SCREAMING_SNAKE_CASE :int , SCREAMING_SNAKE_CAS... | 232 | 0 |
'''simple docstring'''
import unittest
from transformers import SPIECE_UNDERLINE, XLNetTokenizer, XLNetTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
lowerCamelCase : Tuple =... | 47 |
"""simple docstring"""
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_uti... | 91 | 0 |
import argparse
import pickle
import numpy as np
import torch
from torch import nn
from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging
logging.set_verbosity_info()
def _a ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase=None ) ... | 265 |
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import Stabl... | 265 | 1 |
'''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... | 58 |
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 : List[str] = logging.get_logger(__name__)
_lowerCAmelCase : O... | 300 | 0 |
"""simple docstring"""
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 timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transform... | 154 | """simple docstring"""
import unittest
from transformers import SPIECE_UNDERLINE
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.tokenization_utils import AddedToken
from ... | 154 | 1 |
'''simple docstring'''
import warnings
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 : int = logging.get_logger(__name__)
... | 2 |
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelerate_available,
is_accel... | 236 | 0 |
'''simple docstring'''
import unittest
from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, rand... | 367 |
'''simple docstring'''
# 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 a__ ( lowerCAmelCase__ ) -> List[Any]:
return 1 / (1 + np.exp(-z ... | 299 | 0 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiec... | 107 |
def __SCREAMING_SNAKE_CASE ( snake_case_ ):
'''simple docstring'''
_UpperCAmelCase = len(snake_case_ )
for i in range(snake_case_ ):
for j in range(i + 1 , snake_case_ ):
if numbers[j] < numbers[i]:
... | 133 | 0 |
'''simple docstring'''
import os
import random
import sys
from . import cryptomath_module as cryptoMath # noqa: N812
from . import rabin_miller as rabinMiller # noqa: N812
def UpperCamelCase ( ):
print("Making key files..." )
make_key_files("rsa" , 10_24 )
print("Key files... | 357 |
'''simple docstring'''
import os
import random
import sys
from . import cryptomath_module as cryptoMath # noqa: N812
from . import rabin_miller as rabinMiller # noqa: N812
def UpperCamelCase ( ):
print("Making key files..." )
make_key_files("rsa" , 10_24 )
print("Key files... | 123 | 0 |
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipe... | 51 |
import argparse
import torch
from torch import nn
from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def __lowercase ( a__ ) -> Tuple:
__SCREAMING_SNAKE_CASE = [
'encoder.version',
'decoder.version',
'model.encoder.version',
... | 257 | 0 |
"""simple docstring"""
from math import sqrt
import numpy as np
from sympy import symbols
# Coefficient
# Speed of light (m/s)
__A = 2_9979_2458
# Symbols
__A = symbols('ct x y z')
def _lowerCamelCase(__UpperCamelCase ) -> float:
if velocity > c:
raise ValueError("""S... | 368 |
"""simple docstring"""
import warnings
from .generation import TFGenerationMixin
class lowerCamelCase__ ( __magic_name__ ):
'''simple docstring'''
# warning at import time
warnings.warn(
'''Importing `TFGenerationMixin` from `src/transformers/generation_tf_uti... | 341 | 0 |
def __UpperCamelCase ( _A ):
lowerCAmelCase_ = [[0 for _ in range(_A )] for _ in range(m + 1 )]
for i in range(m + 1 ):
lowerCAmelCase_ = 1
for n in range(m + 1 ):
for k in range(1 , _A ):
memo[n][k] += memo[n][k - 1]
... | 278 |
import os
from typing import List, Optional, Union
from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
from ...utils import logging
_lowerCamelCase : Optional[Any] = logging.get_logger(__name__)
_lowerCamelCase : Dict = {"voca... | 336 | 0 |
from ..utils import DummyObject, requires_backends
class __UpperCAmelCase ( metaclass=lowerCamelCase__ ):
UpperCamelCase = ["""onnx"""]
def __init__( self : int, *__A : Optional[Any], **__A : Dict ):
requires_backends(self, ['''on... | 99 |
from ..utils import DummyObject, requires_backends
class __UpperCAmelCase ( metaclass=lowerCamelCase__ ):
UpperCamelCase = ["""onnx"""]
def __init__( self : int, *__A : Optional[Any], **__A : Dict ):
requires_backends(self, ['''on... | 99 | 1 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__a = {
'configuration_informer': [
'INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'InformerConfig',
],
}
try:
i... | 30 |
"""simple docstring"""
import datasets
from .evaluate import evaluate
_UpperCAmelCase = """\
@inproceedings{Rajpurkar2016SQuAD10,
title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},
author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},
booktitle={EMN... | 173 | 0 |
import unittest
import numpy as np
def _UpperCAmelCase ( SCREAMING_SNAKE_CASE__ : np.ndarray , SCREAMING_SNAKE_CASE__ : np.ndarray , SCREAMING_SNAKE_CASE__ : np.ndarray , SCREAMING_SNAKE_CASE__ : np.ndarray | None = None , ):
__UpperCamelCa... | 117 |
import inspect
import os
import unittest
from dataclasses import dataclass
import torch
from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cuda, require_multi_gpu
from ... | 117 | 1 |
'''simple docstring'''
from __future__ import annotations
a : Tuple = """Muhammad Umer Farooq"""
a : List[Any] = """MIT"""
a : Tuple = """1.0.0"""
a : Union[str, Any] = """Muhammad Umer Farooq"""
a : Tuple = """contact@muhammadumerfarooq.me"""
a ... | 265 |
'''simple docstring'''
from operator import delitem, getitem, setitem
import pytest
from data_structures.hashing.hash_map import HashMap
def __lowerCamelCase ( _lowercase ) -> Optional[Any]:
return getitem, k
def __lowerCamelCase ( _lowercase , _lowercase ) ... | 265 | 1 |
from .constants import (
MODEL_NAME,
OPTIMIZER_NAME,
RNG_STATE_NAME,
SAFE_WEIGHTS_INDEX_NAME,
SAFE_WEIGHTS_NAME,
SCALER_NAME,
SCHEDULER_NAME,
TORCH_LAUNCH_PARAMS,
WEIGHTS_INDEX_NAME,
WEIGHTS_NAME,
)
from .dataclasses import (
BnbQuantizationConfig,
... | 347 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__UpperCamelCase : Optional[Any] = {
"configuration_wav2vec2": ["WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP... | 347 | 1 |
import argparse
import gc
import json
import os
import re
import torch
from huggingface_hub import hf_hub_download
from transformers import AutoModelForCausalLM, AutoTokenizer, PreTrainedTokenizerFast, RwkvConfig
from transformers.modeling_utils import WEIGHTS_INDEX_NAME, shard_checkpoint
__A : Optional[A... | 154 |
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging
__A : Any = logging.get_logger(__name__)
__A : ... | 154 | 1 |
import math
def _A ( __magic_name__ ):
lowercase__ = math.loga(math.sqrt(4 * positive_integer + 1 ) / 2 + 1 / 2 )
return exponent == int(__magic_name__ )
def _A ( __magic_name__ = 1 / 1_2345 ):
lowercase__ = 0
lowercase__ = 0
lowercase__ ... | 201 |
from collections import Counter
from pathlib import Path
from typing import Optional, Tuple
import yaml
class lowerCAmelCase ( yaml.SafeLoader ):
def UpperCAmelCase ( self :Optional[Any] , _lowercase :Any ):
'''simple docstring'''
lowercase__ ... | 201 | 1 |
import importlib
import inspect
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
_A : Optional[Any] = 'src/transformers'
# This is to make sure the transformers module imported is t... | 142 |
import inspect
import unittest
import numpy as np
from transformers import BeitConfig
from transformers.testing_utils import require_flax, require_vision, slow
from transformers.utils import cached_property, is_flax_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...te... | 142 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase = logging.get_logger(__name__)
lowerCamelCase = {
"""weiweishi/roc-bert-base-zh""": """https://huggingface.co/weiweishi/roc-bert-base-zh/resolve... | 241 |
"""simple docstring"""
from maths.prime_check import is_prime
def a__ ( lowerCAmelCase__ ):
if not isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
UpperCAmelCase_ = f"""Input value of [number={number}] must be an integer"""
... | 241 | 1 |
"""simple docstring"""
from __future__ import annotations
from typing import TypedDict
class lowercase__ ( snake_case__ ):
_UpperCAmelCase :str
_UpperCAmelCase :int
def _snake_case ( lowerCamelCase__ : str ) -> list[str]:
if not isinstance(lowerCamelCase__ ... | 144 |
"""simple docstring"""
import json
import os
from dataclasses import dataclass
from functools import partial
from typing import Callable
import flax.linen as nn
import jax
import jax.numpy as jnp
import joblib
import optax
import wandb
from flax import jax_utils, struct, traverse_util
from flax.serialization... | 144 | 1 |
# Copyright (c) 2021-, NVIDIA CORPORATION. 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 requ... | 335 |
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,
... | 335 | 1 |
import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple
import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ne... | 95 |
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : int , _lowerCamelCase : int) -> float:
'''simple docstring'''
return base * power(_lowerCamelCase , (exponent - 1)) if exponent else 1
if __name__ == "__main__":
print('Raise base to the power of e... | 232 | 0 |
from __future__ import annotations
import unittest
from transformers import MobileBertConfig, is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModel... | 304 |
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 | 1 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_videomae import VideoMAEImageProcessor
_lowercase : int = logging.get_logger(__name__)
class _UpperCAmelCase ( _lowerCAmelCase ):
def __init__( self : Union[str, Any... | 332 |
"""simple docstring"""
from typing import Dict
from .base import GenericTensor, Pipeline
class _UpperCAmelCase ( _lowerCAmelCase ):
def a ( self : Tuple , _lowercase : Dict=None , _lowercase : str=None , _lowercase : Union[str, Any]=... | 332 | 1 |
'''simple docstring'''
def _A ( snake_case , snake_case ) -> int:
while b:
_lowercase , _lowercase : Optional[int] = b, a % b
return a
def _A ( snake_case , snake_case ) -> int:
return a if b == 0 else euclidean_g... | 199 |
'''simple docstring'''
import argparse
import random
import joblib
import numpy as np
import torch
from igf.igf import (
SecondaryLearner,
collect_objective_set,
compute_perplexity,
generate_datasets,
load_gpta,
recopy_gpta,
set_seed,
train_secondary_learner,
)
from torch.utils.data i... | 199 | 1 |
'''simple docstring'''
import logging
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
BERT_START_DOCS... | 67 |
"""simple docstring"""
from typing import Dict, Iterable, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
res... | 246 | 0 |
from string import ascii_uppercase
UpperCamelCase = {char: i for i, char in enumerate(ascii_uppercase)}
UpperCamelCase = dict(enumerate(ascii_uppercase))
def __lowerCamelCase ( snake_case__ ,snake_case__ ):
"""simple docstring"""
_SCRE... | 357 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
UpperCamelCase = {
'''configuration_layoutlmv3''': [
'''L... | 125 | 0 |
'''simple docstring'''
import os
import unittest
from transformers.models.bartpho.tokenization_bartpho import VOCAB_FILES_NAMES, BartphoTokenizer
from transformers.testing_utils import get_tests_dir
from ...test_tokenization_common import TokenizerTesterMixin
lowerCamelCase__ = get_tests_d... | 234 |
'''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 DDIMSchedulerOut... | 208 | 0 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ... | 55 |
'''simple docstring'''
def a_ ( lowerCamelCase : list[int] ):
if not nums: # Makes sure that the list is not empty
raise ValueError('List is empty' )
lowerCAmelCase = sum(lowerCamelCase ) / len(lowerCamelCase ) # Calculate the average
return sum(... | 55 | 1 |
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