code stringlengths 87 55.2k | code_codestyle int64 0 349 | style_context stringlengths 135 49.1k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionMo... | 40 |
"""simple docstring"""
import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional
import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto import tqdm... | 81 | 0 |
'''simple docstring'''
from __future__ import annotations
def SCREAMING_SNAKE_CASE_ (UpperCamelCase , UpperCamelCase ) -> list[str]:
if nth_term == "":
return [""]
lowerCamelCase__ : Any = int(UpperCamel... | 41 |
"""simple docstring"""
def _A ( ):
"""simple docstring"""
for n in range(1 , 1_00_00_00 ):
yield n * (n + 1) // 2
def _A ( lowercase ):
"""simple docstring"""
a =1
a =2
while i * i <= n:
... | 81 | 0 |
'''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 (
Channe... | 42 |
"""simple docstring"""
from math import factorial
def _A ( lowercase = 1_00 ):
"""simple docstring"""
return sum(int(lowercase ) for x in str(factorial(lowercase ) ) )
if __name__ == "__main__":
print(solution(int(input("""Enter the Number: """).... | 81 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
__lowercase = {'''processing_layoutxlm''': ['''LayoutXLMProcessor''']}
try:
if n... | 43 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
lowerCamelCase_ : Any = logging.get_... | 81 | 0 |
"""simple docstring"""
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def SCREAMING_SNAKE_CASE ( ) -> str:
_lowerCAmelCase : Optional[Any] = ArgumentParser(... | 44 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
lowerCamelCase_ : Any = logging.get_... | 81 | 0 |
"""simple docstring"""
def lowercase ( lowerCAmelCase__ : int = 600851475143 ) -> int:
try:
__a = int(lowerCAmelCase__ )
except (TypeError, ValueError):
raise TypeError('''Parameter n must be int or castable to int.''' )
if n <= 0:
raise ... | 45 |
"""simple docstring"""
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union
# Build the list of all image processors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic... | 81 | 0 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_flava import FlavaImageProcessor
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
class lowercase ( _UpperCAmelCase ):
def __init__( self , *lowercase , ... | 46 |
"""simple docstring"""
from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow
from ..bert.test_tokenization_bert import BertTokenizationTest
@require_tokenizers
class __A ( _SCREAMING_SNA... | 81 | 0 |
'''simple docstring'''
from __future__ import annotations
from statistics import mean
def _lowerCAmelCase ( _UpperCamelCase : list[int] , _UpperCamelCase : list[int] , _UpperCamelCase : int ) -> list[int]:
"""simple docstring"""
_SCREAM... | 47 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_tf_available,
is_torch_available,
)
lowerCamelCase_ : List[str] = {
... | 81 | 0 |
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 A ( _SCREAMING_SNAKE_CASE ) -> tuple:
return (... | 48 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase_ : Union[str, Any] = {
"""configuration_table_transformer""": [
"""TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHI... | 81 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__snake_case :str = {'''configuration_yolos''': ['''YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''YolosConfig''', '''YolosOnnxConfig''']}
try:
if not is_vision... | 49 |
"""simple docstring"""
from __future__ import annotations
import math
def _A ( lowercase ):
"""simple docstring"""
if num <= 0:
a =f'''{num}: Invalid input, please enter a positive integer.'''
raise ValueError(lowercase )
a =[Tr... | 81 | 0 |
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase , _UpperCAmelCase ) -> float:
_validate_point(_UpperCAmelCase )
_validate_point(_UpperCAmelCase )
if len(_UpperCAmelCase ) != len(_UpperCAmelCase ):
raise ValueError('Both points must be in the same n-dimensional space' )
... | 50 |
"""simple docstring"""
import os
import unittest
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import (
VOCAB_FILES_NAMES,
BasicTokenizer,
BertTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whites... | 81 | 0 |
from typing import List, Optional, Union
import numpy as np
import tensorflow as tf
from .utils import logging
snake_case_ : List[Any] = logging.get_logger(__name__)
def A (__A : Union[tf.Tensor, np.ndarray] ) -> List[int]:
"""simple do... | 51 |
"""simple docstring"""
import copy
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
lowerCame... | 81 | 0 |
__lowerCamelCase : Union[str, Any] = {
"""meter""": """m""",
"""kilometer""": """km""",
"""megametre""": """Mm""",
"""gigametre""": """Gm""",
"""terametre""": """Tm""",
"""petametre""": """Pm""",
"""exametre""": """Em""",
"""zettametre""": """Zm""",
"""yottametr... | 52 |
"""simple docstring"""
def _A ( lowercase , lowercase ):
"""simple docstring"""
while second != 0:
a =first & second
first ^= second
a =c << 1
return first
if __name__ == "__main__":
import doctest
... | 81 | 0 |
'''simple docstring'''
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis_float32 (there's also the fix_lavis branch)
# also note: to convert Vicuna checkpoints, we... | 53 |
"""simple docstring"""
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def _A ( ):
"""simple docstring"""
a =ArgumentParser(
description=(
... | 81 | 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
a__ : Optional[int] = get_te... | 54 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
lowerCamelCase_ : str = logging.get_logger(__name__)
class __A ( _SCREAMING_SNAKE_CASE ):
"""simple docstring""... | 81 | 0 |
'''simple docstring'''
def __snake_case ( ):
return [
a * b * (1000 - a - b)
for a in range(1 , 999 )
for b in range(UpperCAmelCase_ , 999 )
if (a * a + b * b == (1000 - a - b) ** 2)
][0]
if __name__ == "__main__":
print(f'''{solution() = }''')
... | 55 |
"""simple docstring"""
def _A ( lowercase , lowercase ):
"""simple docstring"""
return number | (1 << position)
def _A ( lowercase , lowercase ):
"""simple docstring"""
return number & ~(1 << position)
def _A ... | 81 | 0 |
'''simple docstring'''
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_ax... | 56 |
"""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 transformers import AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification
def _A ( ... | 81 | 0 |
"""simple docstring"""
import argparse
import logging
import sys
from unittest.mock import patch
import run_glue_deebert
from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow
logging.basicConfig(level=logging.DEBUG)
A : str = logging.getLogger()
... | 57 |
"""simple docstring"""
lowerCamelCase_ : Any = [
"""DownloadConfig""",
"""DownloadManager""",
"""DownloadMode""",
"""StreamingDownloadManager""",
]
from .download_config import DownloadConfig
from .download_manager import DownloadManager, DownloadMode
from .st... | 81 | 0 |
'''simple docstring'''
import importlib
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Union
import torch
from ..utils import BaseOutput
lowercase_ = """scheduler_config.json"""
class a_ ( snake_case_ ):
'''simple docstri... | 58 |
"""simple docstring"""
from scipy.stats import pearsonr
import datasets
lowerCamelCase_ : Optional[int] = """
Pearson correlation coefficient and p-value for testing non-correlation.
The Pearson correlation coefficient measures the linear relationship between two datasets. The ... | 81 | 0 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless requir... | 59 |
"""simple docstring"""
lowerCamelCase_ : int = """
# Installazione di Transformers
! pip install transformers datasets
# Per installare dalla fonte invece dell'ultima versione rilasciata, commenta il comando sopra e
# rimuovi la modalità commento al comando seguente.
# ! pip install gi... | 81 | 0 |
"""simple docstring"""
import logging
import os
from typing import Dict, List, Optional, Union
import torch
import torch.nn as nn
from accelerate.utils.imports import (
is_abit_bnb_available,
is_abit_bnb_available,
is_bnb_available,
)
from ..big_modeling import dispatch_model, init_empty_weights
... | 60 |
"""simple docstring"""
def _A ( lowercase = "The quick brown fox jumps over the lazy dog" , ):
"""simple docstring"""
a =set()
# Replace all the whitespace in our sentence
a =input_str.replace(''' ''' , '''''' )
for alpha in ... | 81 | 0 |
"""simple docstring"""
_a = 9.8_0665
def __a ( __lowerCamelCase, __lowerCamelCase, __lowerCamelCase = g ):
if fluid_density <= 0:
raise ValueError("Impossible fluid density" )
if volume < 0:
raise ValueError("Impossible Object volume" )
if gravity <= 0:
... | 61 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
lowerCamelCase_ : Optional[Any] = {}
try:
... | 81 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_A = {'configuration_glpn': ['GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GLPNConfig']}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailabl... | 62 |
"""simple docstring"""
import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional
import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto import tqdm... | 81 | 0 |
'''simple docstring'''
import re
def _lowerCamelCase ( lowercase : str ) -> str:
if len(re.findall("[ATCG]" , lowercase ) ) != len(lowercase ):
raise ValueError("Invalid Strand" )
return dna.translate(dna.maketrans("ATCG" , "T... | 63 |
"""simple docstring"""
def _A ( ):
"""simple docstring"""
for n in range(1 , 1_00_00_00 ):
yield n * (n + 1) // 2
def _A ( lowercase ):
"""simple docstring"""
a =1
a =2
while i * i <= n:
... | 81 | 0 |
"""simple docstring"""
import importlib
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Union
import torch
from ..utils import BaseOutput
A_ = '''scheduler_config.json'''
class lowercase( __a ):
... | 64 |
"""simple docstring"""
from math import factorial
def _A ( lowercase = 1_00 ):
"""simple docstring"""
return sum(int(lowercase ) for x in str(factorial(lowercase ) ) )
if __name__ == "__main__":
print(solution(int(input("""Enter the Number: """).... | 81 | 0 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
PNDMScheduler,
StableDiffusionLDMaDPipeline,
UNetaDConditionModel,
)
from diffusers.utils import night... | 65 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
lowerCamelCase_ : Any = logging.get_... | 81 | 0 |
"""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 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
lowerCamelCase_ : Any = logging.get_... | 81 | 0 |
'''simple docstring'''
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 uti... | 67 |
"""simple docstring"""
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union
# Build the list of all image processors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic... | 81 | 0 |
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import BatchEncoding, MarianTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import is_sentencepiece_available, is_tf_available, is_torch_available... | 68 |
"""simple docstring"""
from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow
from ..bert.test_tokenization_bert import BertTokenizationTest
@require_tokenizers
class __A ( _SCREAMING_SNA... | 81 | 0 |
"""simple docstring"""
from diffusers.utils.testing_utils import require_onnxruntime
@require_onnxruntime
class UpperCamelCase :
pass
| 69 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_tf_available,
is_torch_available,
)
lowerCamelCase_ : List[str] = {
... | 81 | 0 |
'''simple docstring'''
# 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
f... | 70 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase_ : Union[str, Any] = {
"""configuration_table_transformer""": [
"""TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHI... | 81 | 0 |
from manim import *
class __A ( a ):
"""simple docstring"""
def __lowercase ( self ):
"""simple docstring"""
__UpperCamelCase : Optional[Any] =Rectangle(height=0.5 , width=0.5 )
... | 71 |
"""simple docstring"""
from __future__ import annotations
import math
def _A ( lowercase ):
"""simple docstring"""
if num <= 0:
a =f'''{num}: Invalid input, please enter a positive integer.'''
raise ValueError(lowercase )
a =[Tr... | 81 | 0 |
"""simple docstring"""
import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch
from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search
from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
from u... | 72 |
"""simple docstring"""
import os
import unittest
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import (
VOCAB_FILES_NAMES,
BasicTokenizer,
BertTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whites... | 81 | 0 |
import argparse
from tax import checkpoints
from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) -> Any:
__lowerCamelCase : Optional[Any] = AutoConfig.from_pretraine... | 73 |
"""simple docstring"""
import copy
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
lowerCame... | 81 | 0 |
"""simple docstring"""
# limitations under the License.
from typing import Optional, Tuple, Union
import torch
from diffusers import DiffusionPipeline, ImagePipelineOutput
class lowerCAmelCase_ ( _lowercase ):
'''simple docstring'''
def __init__( self : int ,A_ : ... | 74 |
"""simple docstring"""
def _A ( lowercase , lowercase ):
"""simple docstring"""
while second != 0:
a =first & second
first ^= second
a =c << 1
return first
if __name__ == "__main__":
import doctest
... | 81 | 0 |
'''simple docstring'''
import itertools
import math
def a_ ( __snake_case : int ) -> bool:
"""simple docstring"""
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 =... | 75 |
"""simple docstring"""
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def _A ( ):
"""simple docstring"""
a =ArgumentParser(
description=(
... | 81 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ = {
'configuration_blip_2': [
'BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP',
'Blip2Config',
'Blip2QFormerConfig',
'Blip2VisionConfig',
],
'processing_b... | 76 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
lowerCamelCase_ : str = logging.get_logger(__name__)
class __A ( _SCREAMING_SNAKE_CASE ):
"""simple docstring""... | 81 | 0 |
"""simple docstring"""
from collections import Counter
from timeit import timeit
def a_ ( _lowerCAmelCase : str = "" , ):
'''simple docstring'''
return sum(c % 2 for c in Counter(input_str.replace(' ' , '' ).lower() ).values() ) < 2
def a_ ( ... | 77 |
"""simple docstring"""
def _A ( lowercase , lowercase ):
"""simple docstring"""
return number | (1 << position)
def _A ( lowercase , lowercase ):
"""simple docstring"""
return number & ~(1 << position)
def _A ... | 81 | 0 |
"""simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case_ = logging.get_logger(__name__)
snake_case_ = {
"""asapp/sew-d-tiny-100k""": """https://huggingface.co/asapp/sew-... | 78 |
"""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 transformers import AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification
def _A ( ... | 81 | 0 |
'''simple docstring'''
import argparse
import fairseq
import torch
from torch import nn
from transformers import (
MBartaaTokenizer,
MBartConfig,
MBartForCausalLM,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaModel,... | 79 |
"""simple docstring"""
lowerCamelCase_ : Any = [
"""DownloadConfig""",
"""DownloadManager""",
"""DownloadMode""",
"""StreamingDownloadManager""",
]
from .download_config import DownloadConfig
from .download_manager import DownloadManager, DownloadMode
from .st... | 81 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a__ : Any = logging.get_logger(__name__)
a__ : Dict = {
'facebook/s2t-wav2vec2-large-en-de': (
'https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/re... | 80 |
"""simple docstring"""
from scipy.stats import pearsonr
import datasets
lowerCamelCase_ : Optional[int] = """
Pearson correlation coefficient and p-value for testing non-correlation.
The Pearson correlation coefficient measures the linear relationship between two datasets. The ... | 81 | 0 |
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,
AutoTokenizer,
Da... | 82 |
"""simple docstring"""
lowerCamelCase_ : int = """
# Installazione di Transformers
! pip install transformers datasets
# Per installare dalla fonte invece dell'ultima versione rilasciata, commenta il comando sopra e
# rimuovi la modalità commento al comando seguente.
# ! pip install gi... | 81 | 0 |
'''simple docstring'''
def A__ ( UpperCAmelCase_ = 1_0_0_0 ):
_UpperCamelCase : Dict = 3
_UpperCamelCase : Any = 0
while a < n:
if a % 3 == 0 or a % 5 == 0:
result += a
elif a % 1_5 == 0:
result -= a
... | 83 |
"""simple docstring"""
def _A ( lowercase = "The quick brown fox jumps over the lazy dog" , ):
"""simple docstring"""
a =set()
# Replace all the whitespace in our sentence
a =input_str.replace(''' ''' , '''''' )
for alpha in ... | 81 | 0 |
"""simple docstring"""
from decimal import Decimal, getcontext
from math import ceil, factorial
def _snake_case ( lowercase__ : int ) -> str:
'''simple docstring'''
if not isinstance(lowercase__ , lowercase__ ):
raise TypeError("""Un... | 84 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
lowerCamelCase_ : Optional[Any] = {}
try:
... | 81 | 0 |
'''simple docstring'''
import argparse
import json
import numpy
import torch
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def UpperCamelCase_( snake_case : Optional[i... | 85 |
"""simple docstring"""
import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional
import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto import tqdm... | 81 | 0 |
"""simple docstring"""
def __lowerCAmelCase (_UpperCamelCase ):
if not head:
return True
# split the list to two parts
__lowerCAmelCase , __lowerCAmelCase : Optional[int] = head.next, head
while fast and fast.next:
__lowerCAmelCase : Any = fast.next.ne... | 86 |
"""simple docstring"""
def _A ( ):
"""simple docstring"""
for n in range(1 , 1_00_00_00 ):
yield n * (n + 1) // 2
def _A ( lowercase ):
"""simple docstring"""
a =1
a =2
while i * i <= n:
... | 81 | 0 |
import random
from typing import Any
def lowercase_ ( _lowerCamelCase : list):
for _ in range(len(_lowerCamelCase)):
lowercase__ : Dict = random.randint(0 , len(_lowerCamelCase) - 1)
lowercase__ : Union[str, Any] = random.randint(0 , len(_lowerC... | 87 |
"""simple docstring"""
from math import factorial
def _A ( lowercase = 1_00 ):
"""simple docstring"""
return sum(int(lowercase ) for x in str(factorial(lowercase ) ) )
if __name__ == "__main__":
print(solution(int(input("""Enter the Number: """).... | 81 | 0 |
def a__ ( A_ ):
'''simple docstring'''
__magic_name__ = 0
__magic_name__ = len(A_ )
for i in range(n - 1 ):
for j in range(i + 1, A_ ):
if arr[i] > arr[j]:
num_inversions += 1
return num_inversions
... | 88 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
lowerCamelCase_ : Any = logging.get_... | 81 | 0 |
'''simple docstring'''
from typing import List
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCAmelCase = logging.get_logger(__name__)
__lowerCAmelCase = {
'''snap-research/efficientformer-l1-300''': (
'''https://huggingface.co/snap-resea... | 89 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
lowerCamelCase_ : Any = logging.get_... | 81 | 0 |
def lowerCamelCase_ ( UpperCamelCase__ : int = 10 , UpperCamelCase__ : int = 1000 , UpperCamelCase__ : bool = True ) -> int:
"""simple docstring"""
assert (
isinstance(UpperCamelCase__ , UpperCamelCase__ )
... | 90 |
"""simple docstring"""
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union
# Build the list of all image processors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic... | 81 | 0 |
"""simple docstring"""
import unittest
from accelerate import debug_launcher
from accelerate.test_utils import require_cpu, test_ops, test_script
@require_cpu
class lowerCAmelCase__ ( unittest.TestCase ):
'''simple docstring'''
def _SCREAMING_SNAKE_CASE ( self :... | 91 |
"""simple docstring"""
from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow
from ..bert.test_tokenization_bert import BertTokenizationTest
@require_tokenizers
class __A ( _SCREAMING_SNA... | 81 | 0 |
import argparse
import torch
from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging
logging.set_verbosity_info()
def _a ( SCREAMING_SNAKE_CASE_ : Optional[int] , SCREAMING_SNAKE_CASE_ :... | 92 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_tf_available,
is_torch_available,
)
lowerCamelCase_ : List[str] = {
... | 81 | 0 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, XLMRobertaTokenizer
from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.a... | 93 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase_ : Union[str, Any] = {
"""configuration_table_transformer""": [
"""TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHI... | 81 | 0 |
from __future__ import annotations
def __lowerCamelCase ( UpperCAmelCase_ : float , UpperCAmelCase_ : float , UpperCAmelCase_ : float , ):
"""simple docstring"""
if (electron_conc, hole_conc, intrinsic_conc).count(0 ) != 1:
raise ValueError('''You c... | 94 |
"""simple docstring"""
from __future__ import annotations
import math
def _A ( lowercase ):
"""simple docstring"""
if num <= 0:
a =f'''{num}: Invalid input, please enter a positive integer.'''
raise ValueError(lowercase )
a =[Tr... | 81 | 0 |
import json
import os
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common impo... | 95 |
"""simple docstring"""
import os
import unittest
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import (
VOCAB_FILES_NAMES,
BasicTokenizer,
BertTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whites... | 81 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
lowercase__ = {
"""configuration_mega""": ["""MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MegaConfig""", """M... | 96 |
"""simple docstring"""
import copy
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
lowerCame... | 81 | 0 |
'''simple docstring'''
import os
import re
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__snake_case = logging.get_logger(__name__)
__snake_case = {
'''vocab_file''': '''... | 97 |
"""simple docstring"""
def _A ( lowercase , lowercase ):
"""simple docstring"""
while second != 0:
a =first & second
first ^= second
a =c << 1
return first
if __name__ == "__main__":
import doctest
... | 81 | 0 |
"""simple docstring"""
import re
import tempfile
from pathlib import Path
import pytest
import yaml
from datasets.utils.readme import ReadMe
# @pytest.fixture
# def example_yaml_structure():
lowerCAmelCase__ : Any = yaml.safe_load(
'\\nname: ""\nallow_empty: false\nallow_empty_text: true\nsub... | 98 |
"""simple docstring"""
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def _A ( ):
"""simple docstring"""
a =ArgumentParser(
description=(
... | 81 | 0 |
import inspect
import unittest
from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_configuration_common... | 99 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
lowerCamelCase_ : str = logging.get_logger(__name__)
class __A ( _SCREAMING_SNAKE_CASE ):
"""simple docstring""... | 81 | 0 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
PNDMScheduler,
StableDiffusionLDMaDPipeline,
UNetaDConditionModel,
)
from diffusers.uti... | 100 |
"""simple docstring"""
def _A ( lowercase , lowercase ):
"""simple docstring"""
return number | (1 << position)
def _A ( lowercase , lowercase ):
"""simple docstring"""
return number & ~(1 << position)
def _A ... | 81 | 0 |
from tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch
from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER, require_tf, require_torch
@require_torch
... | 101 |
"""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 transformers import AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification
def _A ( ... | 81 | 0 |
"""simple docstring"""
import logging
import torch
from accelerate import Accelerator
from arguments import EvaluationArguments
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from transformers import AutoModelForCausalLM, AutoTokeni... | 102 |
"""simple docstring"""
lowerCamelCase_ : Any = [
"""DownloadConfig""",
"""DownloadManager""",
"""DownloadMode""",
"""StreamingDownloadManager""",
]
from .download_config import DownloadConfig
from .download_manager import DownloadManager, DownloadMode
from .st... | 81 | 0 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A__ : str = {'''configuration_focalnet''': ['''FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FocalNetConfig''']}
try:
if not is_torch_ava... | 103 |
"""simple docstring"""
from scipy.stats import pearsonr
import datasets
lowerCamelCase_ : Optional[int] = """
Pearson correlation coefficient and p-value for testing non-correlation.
The Pearson correlation coefficient measures the linear relationship between two datasets. The ... | 81 | 0 |
'''simple docstring'''
# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation
import warnings
from .state import AcceleratorState, GradientState
warnings.filterwarnings('''ignore''', category=UserWarning, module='''torch.optim.lr_scheduler''')
class ... | 104 |
"""simple docstring"""
lowerCamelCase_ : int = """
# Installazione di Transformers
! pip install transformers datasets
# Per installare dalla fonte invece dell'ultima versione rilasciata, commenta il comando sopra e
# rimuovi la modalità commento al comando seguente.
# ! pip install gi... | 81 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
a : List[str] = {'''configuration_reformer''': ['''REF... | 105 |
"""simple docstring"""
def _A ( lowercase = "The quick brown fox jumps over the lazy dog" , ):
"""simple docstring"""
a =set()
# Replace all the whitespace in our sentence
a =input_str.replace(''' ''' , '''''' )
for alpha in ... | 81 | 0 |
"""simple docstring"""
import argparse
import json
import os
import torch
from transformers.file_utils import has_file
from diffusers import UNetaDConditionModel, UNetaDModel
__UpperCamelCase : List[str] = False
__UpperCamelCase : List[Any] = True
__UpperCamelCase : ... | 106 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
lowerCamelCase_ : Optional[Any] = {}
try:
... | 81 | 0 |
from __future__ import annotations
def __magic_name__ ( A : int ):
'''simple docstring'''
a = str(A )
return len(A ) == 9 and set(A ) == set("123456789" )
def __magic_name__ ( ):
'''simple docstring'''
for base_num in range(9999, 499... | 107 |
"""simple docstring"""
import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional
import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto import tqdm... | 81 | 0 |
"""simple docstring"""
from statistics import mean
import numpy as np
def a__ ( SCREAMING_SNAKE_CASE : list , SCREAMING_SNAKE_CASE : list , SCREAMING_SNAKE_CASE : list , SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
lowerCAmelCa... | 108 |
"""simple docstring"""
def _A ( ):
"""simple docstring"""
for n in range(1 , 1_00_00_00 ):
yield n * (n + 1) // 2
def _A ( lowercase ):
"""simple docstring"""
a =1
a =2
while i * i <= n:
... | 81 | 0 |
"""simple docstring"""
from math import pi
def _snake_case ( UpperCamelCase : int , UpperCamelCase : int ):
return 2 * pi * radius * (angle / 360)
if __name__ == "__main__":
print(arc_length(9_0, 1_0))
| 109 |
"""simple docstring"""
from math import factorial
def _A ( lowercase = 1_00 ):
"""simple docstring"""
return sum(int(lowercase ) for x in str(factorial(lowercase ) ) )
if __name__ == "__main__":
print(solution(int(input("""Enter the Number: """).... | 81 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaImgaImgPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, ... | 110 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
lowerCamelCase_ : Any = logging.get_... | 81 | 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.conversational im... | 250 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
lowerCamelCase_ : Any = logging.get_... | 81 | 0 |
'''simple docstring'''
import fcntl
import os
import socket
import torch
import torch.distributed as dist
def __UpperCAmelCase ( *A : int ) -> int:
with open(A , '''r''' ) as fh:
fcntl.flock(A , fcntl.LOCK_EX )
try:
print(*A )
... | 304 |
"""simple docstring"""
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union
# Build the list of all image processors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic... | 81 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase__ = {"""configuration_vit_msn""": ["""VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ViTMSNConfig"""]}
try:
if not is_torch_availabl... | 181 |
"""simple docstring"""
from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow
from ..bert.test_tokenization_bert import BertTokenizationTest
@require_tokenizers
class __A ( _SCREAMING_SNA... | 81 | 0 |
"""simple docstring"""
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
lowerCamelCase_ = """\
@inproceedings{wang2019glue,
title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},
... | 191 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_tf_available,
is_torch_available,
)
lowerCamelCase_ : List[str] = {
... | 81 | 0 |
"""simple docstring"""
import argparse
import os
import re
import packaging.version
__magic_name__ = """examples/"""
__magic_name__ = {
"""examples""": (re.compile(R"^check_min_version\(\"[^\"]+\"\)\s*$", re.MULTILINE), """check_min_version(\"VERSION\")\n"""),
"""init""": (re.compile(R"... | 100 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase_ : Union[str, Any] = {
"""configuration_table_transformer""": [
"""TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHI... | 81 | 0 |
import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging
if is_torch_available():
import torch
... | 118 |
"""simple docstring"""
from __future__ import annotations
import math
def _A ( lowercase ):
"""simple docstring"""
if num <= 0:
a =f'''{num}: Invalid input, please enter a positive integer.'''
raise ValueError(lowercase )
a =[Tr... | 81 | 0 |
from __future__ import annotations
from collections.abc import Generator
import requests
from bsa import BeautifulSoup
__snake_case = """https://www.indeed.co.in/jobs?q=mobile+app+development&l="""
def lowerCAmelCase_ ( __lowerCAmelCase = "mumbai" )-> Optional[int]:
'''simple ... | 348 |
"""simple docstring"""
import os
import unittest
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import (
VOCAB_FILES_NAMES,
BasicTokenizer,
BertTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whites... | 81 | 0 |
from ...utils import is_torch_available, is_transformers_available
if is_transformers_available() and is_torch_available():
from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline
| 339 |
"""simple docstring"""
import copy
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
lowerCame... | 81 | 0 |
import json
import os
from datetime import date
from pathlib import Path
from tabulate import DataRow, TableFormat, tabulate
a__ = TableFormat(
lineabove=None,
linebelowheader=None,
linebetweenrows=None,
linebelow=None,
headerrow=DataRow('''''', '''|''', '''|'''),
dataro... | 235 |
"""simple docstring"""
def _A ( lowercase , lowercase ):
"""simple docstring"""
while second != 0:
a =first & second
first ^= second
a =c << 1
return first
if __name__ == "__main__":
import doctest
... | 81 | 0 |
'''simple docstring'''
import os
from typing import BinaryIO, Optional, Union
import numpy as np
import pyarrow.parquet as pq
from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
from ..packaged_modules i... | 83 |
"""simple docstring"""
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def _A ( ):
"""simple docstring"""
a =ArgumentParser(
description=(
... | 81 | 0 |
"""simple docstring"""
import argparse
lowercase__ = """docs/source/_static/js/custom.js"""
def __a ( _SCREAMING_SNAKE_CASE ) ->Union[str, Any]:
with open(_SCREAMING_SNAKE_CASE , encoding='utf-8' , newline='\n' ) as f:
a__: Any = f.readlines()
a__: Opti... | 290 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
lowerCamelCase_ : str = logging.get_logger(__name__)
class __A ( _SCREAMING_SNAKE_CASE ):
"""simple docstring""... | 81 | 0 |
'''simple docstring'''
def _A ( snake_case ) -> List[str]:
for i in range(len(snake_case ) - 1 , 0 , -1 ):
_lowercase : Tuple = False
for j in range(snake_case , 0 , -1 ):
if unsorted[j] < unsorted[j - 1]:
_lowerc... | 250 |
"""simple docstring"""
def _A ( lowercase , lowercase ):
"""simple docstring"""
return number | (1 << position)
def _A ( lowercase , lowercase ):
"""simple docstring"""
return number & ~(1 << position)
def _A ... | 81 | 0 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class snake_case__ ( metaclass=_SCREAMING_SNAKE_CASE):
a_ = ["torch", "transformers", "onnx"]
def __init__( self : Any , *_A : List[str] , **_A : Union[str, Any... | 304 |
"""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 transformers import AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification
def _A ( ... | 81 | 0 |
'''simple docstring'''
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__ = logging.get_logger(__name__)
UpperCamelCase__ = {
"""microsoft/unispeech-large-1500h-cv""": (
"""https:/... | 181 |
"""simple docstring"""
lowerCamelCase_ : Any = [
"""DownloadConfig""",
"""DownloadManager""",
"""DownloadMode""",
"""StreamingDownloadManager""",
]
from .download_config import DownloadConfig
from .download_manager import DownloadManager, DownloadMode
from .st... | 81 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
lowerCamelCase_ = {"""configuration_fnet""": ["""FNET_PRETRAINED_C... | 191 |
"""simple docstring"""
from scipy.stats import pearsonr
import datasets
lowerCamelCase_ : Optional[int] = """
Pearson correlation coefficient and p-value for testing non-correlation.
The Pearson correlation coefficient measures the linear relationship between two datasets. The ... | 81 | 0 |
"""simple docstring"""
from ..models.whisper import WhisperForConditionalGeneration, WhisperProcessor
from .base import PipelineTool
class SCREAMING_SNAKE_CASE_ ( _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
__lowercase : int = '''openai/whisper-base'''
__lowercase ... | 100 |
"""simple docstring"""
lowerCamelCase_ : int = """
# Installazione di Transformers
! pip install transformers datasets
# Per installare dalla fonte invece dell'ultima versione rilasciata, commenta il comando sopra e
# rimuovi la modalità commento al comando seguente.
# ! pip install gi... | 81 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A : Dict = logging.get_logger(__name__)
A : List[Any] = {
"""facebook/nllb-moe-54B""": """https://huggingface.co/facebook/nllb-moe-54b/resolve/main/config.json""",
}
class lowerCamelCase (_SCR... | 118 |
"""simple docstring"""
def _A ( lowercase = "The quick brown fox jumps over the lazy dog" , ):
"""simple docstring"""
a =set()
# Replace all the whitespace in our sentence
a =input_str.replace(''' ''' , '''''' )
for alpha in ... | 81 | 0 |
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union
# Build the list of all image processors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...image_pro... | 348 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
lowerCamelCase_ : Optional[Any] = {}
try:
... | 81 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
UpperCAmelCase__ = {
"""configuration_chinese_clip""": [
"""CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""ChineseCLIPConfig""",
"""Chi... | 339 |
"""simple docstring"""
import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional
import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto import tqdm... | 81 | 0 |
def __UpperCAmelCase ( __a : List[str] ,__a : Dict ) -> Optional[Any]:
"""simple docstring"""
if b == 0:
return 1
if (b % 2) == 0:
return actual_power(__a ,int(b / 2 ) ) * actual_power(__a ,int(b / 2 ) )
else:
return a * actual_p... | 235 |
"""simple docstring"""
def _A ( ):
"""simple docstring"""
for n in range(1 , 1_00_00_00 ):
yield n * (n + 1) // 2
def _A ( lowercase ):
"""simple docstring"""
a =1
a =2
while i * i <= n:
... | 81 | 0 |
'''simple docstring'''
import os
import pytest
from attr import dataclass
snake_case_ : Dict = """us-east-1""" # defaults region
@dataclass
class lowercase__ :
lowercase__ = 42
lowercase__ = """arn:aws:iam::558105141721:role/sagemaker_execution_role"""
lowercase__ = ... | 83 |
"""simple docstring"""
from math import factorial
def _A ( lowercase = 1_00 ):
"""simple docstring"""
return sum(int(lowercase ) for x in str(factorial(lowercase ) ) )
if __name__ == "__main__":
print(solution(int(input("""Enter the Number: """).... | 81 | 0 |
"""simple docstring"""
import re
from ..models.auto import AutoProcessor
from ..models.vision_encoder_decoder import VisionEncoderDecoderModel
from ..utils import is_vision_available
from .base import PipelineTool
if is_vision_available():
from PIL import Image
class __snake_case ( _SCREAMING_SNAKE... | 290 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
lowerCamelCase_ : Any = logging.get_... | 81 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any
@dataclass
class a__ :
_SCREAMING_SNAKE_CASE : Optional[Any] = 42
_SCREAMING_SNAKE_CASE : Option... | 250 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
lowerCamelCase_ : Any = logging.get_... | 81 | 0 |
'''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,
tra... | 304 |
"""simple docstring"""
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union
# Build the list of all image processors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic... | 81 | 0 |
'''simple docstring'''
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ ) -> Optional[Any]:
if mass < 0:
raise ValueError('''The mass of a body cannot be negative''' )
return 0.5 * mass * abs(lowerCAmelCase__ ) * abs(lowerCAmelCase__ )
if __name__ =... | 181 |
"""simple docstring"""
from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow
from ..bert.test_tokenization_bert import BertTokenizationTest
@require_tokenizers
class __A ( _SCREAMING_SNA... | 81 | 0 |
"""simple docstring"""
import random
import timeit
from functools import wraps
from typing import Callable, Optional
from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_tf_auto import TF_MODEL_MAPPING, TF_MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available... | 191 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_tf_available,
is_torch_available,
)
lowerCamelCase_ : List[str] = {
... | 81 | 0 |
"""simple docstring"""
from diffusers.utils.testing_utils import require_onnxruntime
@require_onnxruntime
class SCREAMING_SNAKE_CASE_ :
"""simple docstring"""
pass
| 100 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase_ : Union[str, Any] = {
"""configuration_table_transformer""": [
"""TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHI... | 81 | 0 |
import math
import random
def a__ ( __UpperCamelCase , __UpperCamelCase = False ):
if deriv:
return value * (1 - value)
return 1 / (1 + math.exp(-value ))
# Initial Value
A : str = 0.02
def a__ ( __UpperCamelCase , __UpperCamelCase ):
... | 118 |
"""simple docstring"""
from __future__ import annotations
import math
def _A ( lowercase ):
"""simple docstring"""
if num <= 0:
a =f'''{num}: Invalid input, please enter a positive integer.'''
raise ValueError(lowercase )
a =[Tr... | 81 | 0 |
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class __snake_case ( _SCREAMING_SNAKE_CASE ):
__lowerCamelCase : str = ["""image_processor""", """tokenizer"""]
__lowerCamelCase : Dict = """AutoImageProces... | 348 |
"""simple docstring"""
import os
import unittest
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import (
VOCAB_FILES_NAMES,
BasicTokenizer,
BertTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whites... | 81 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_tf_available,
is_torch_available,
)
UpperCAmelCase__ = {
"""configuration_speech_to_text""": ["""SPEECH_TO_TEXT_PRETRAINED_C... | 339 |
"""simple docstring"""
import copy
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
lowerCame... | 81 | 0 |
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