code stringlengths 82 53.2k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
'''simple docstring'''
from math import factorial
def __a ( lowerCAmelCase__ : int = 100 ):
return sum(map(lowerCAmelCase__ , str(factorial(lowerCAmelCase__ ) ) ) )
if __name__ == "__main__":
print(solution(int(input('Enter the Number: ').strip())))
| 688 |
import argparse
import torch
from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging
logging.set_verbosity_info()
def _lowerCAmelCase ( __magic_name__ :Union[str, Any] , __magic_na... | 121 | 0 |
"""simple docstring"""
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorFormatte... | 87 |
"""simple docstring"""
import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset
from ..utils.generic import ModelOutput
class _UpperCAmelCase ( _snake_case):
def __init__( self , snake_case_ , snake_case_ , snake_case_ ):
_snake_c... | 87 | 1 |
"""simple docstring"""
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, ByTaTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...test_tokenization_common impor... | 142 |
"""simple docstring"""
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class lowerCAmelCase ( a ):
"""simple docstring"""
__lowercase :Optional[int] = ["image_processor", "tokenizer"]
__low... | 142 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
snake_case_ : Union[str, Any] = {
"configuration_gpt_bigcode": ["GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTBigCodeConfig"],
}
try:
if not is_torch_available(... | 253 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_lxmert import LxmertTokenizer
snake_case_ : Union[str, Any] = {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}
snake_... | 253 | 1 |
"""simple docstring"""
import argparse
import json
import os
import time
import zipfile
from get_ci_error_statistics import download_artifact, get_artifacts_links
from transformers import logging
a = logging.get_logger(__name__)
def _snake_case ( _snake_case : List[str] , ... | 7 |
"""simple docstring"""
import math
def _snake_case ( _snake_case : float , _snake_case : float ) -> float:
'''simple docstring'''
if (
not isinstance(_snake_case , (int, float) )
or power_factor < -1
or power_fac... | 7 | 1 |
"""simple docstring"""
from copy import deepcopy
import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader
from accelerate.accelerator import Accelerator
from accelerate.state i... | 714 |
"""simple docstring"""
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image
fro... | 378 | 0 |
from functools import lru_cache
@lru_cache
def SCREAMING_SNAKE_CASE__ ( snake_case_ ) -> int:
if num < 0:
raise ValueError('''Number should not be negative.''' )
return 1 if num in (0, 1) else num * factorial(num - 1 )
if __name__ == "__main__":
... | 416 |
def SCREAMING_SNAKE_CASE__ ( snake_case_ = 1_0_0_0 ) -> int:
A__ : Any =3
A__ : Optional[Any] =0
while a < n:
if a % 3 == 0 or a % 5 == 0:
result += a
elif a % 1_5 == 0:
result -= a
a += 1
return result
... | 416 | 1 |
'''simple docstring'''
from typing import Callable, Optional
from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream
class lowercase__ ( __lowerCAmelCase ):
def __init__( self , __UpperCAmelCase , __UpperC... | 718 |
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert import BertTokenizer
a_ = logging.get_logger(__name__)
a_ = {'''v... | 115 | 0 |
"""simple docstring"""
from collections import defaultdict
from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst
def lowercase ( ):
'''simple docstring'''
_UpperCAmelCase , _UpperCAmelCase = 9, 14 # noqa: F841
_Up... | 602 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : List[Any] = logging.get_logger(__name__)
__A : Optional[Any] = {
"google/vivit-b-16x2-kinetics400": (
"https://huggingface.co/google/vivit-b-16x2-kineti... | 602 | 1 |
'''simple docstring'''
import argparse
import json
import os
import re
import torch
from transformers import BloomConfig, BloomModel
from transformers.file_utils import CONFIG_NAME, WEIGHTS_NAME
from transformers.utils import logging
logging.set_verbosity_info()
_lowercase : Tuple = [
"word_emb... | 30 | '''simple docstring'''
from collections import defaultdict
from pathlib import Path
import pandas as pd
from rouge_cli import calculate_rouge_path
from utils import calculate_rouge
_lowercase : Optional[Any] = [
"Prosecutor: \"No videos were used in the crash investigation\" German papers say th... | 30 | 1 |
"""simple docstring"""
import argparse
import json
from tqdm import tqdm
def a_ ( ):
'''simple docstring'''
lowercase__ : str = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--src_path' , type=_lowerCAmelC... | 599 | """simple docstring"""
class UpperCAmelCase_ :
def __init__( self , a , a , a ) -> List[Any]:
lowercase__ : List[str] = name
lowercase__ : List[str] = value
lowercase__ : Tup... | 599 | 1 |
'''simple docstring'''
def UpperCamelCase_ ( A__ : str ):
'''simple docstring'''
lowerCAmelCase_ : List[Any] = hex_num.strip()
if not hex_num:
raise ValueError("""No value was passed to the function""" )
lowerCAmelCase_ : ... | 702 |
'''simple docstring'''
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
__A : Tuple = logging.get_logger(__nam... | 398 | 0 |
"""simple docstring"""
import json
import re
from typing import TYPE_CHECKING, List, Optional, Tuple, Union
import numpy as np
from ...utils import is_tf_available, is_torch_available, logging
if TYPE_CHECKING:
if is_torch_available():
import torch
if is_tf_available():
import... | 29 |
'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments
import transformers
from transformers import (
AutoC... | 444 | 0 |
"""simple docstring"""
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transfor... | 700 |
"""simple docstring"""
import json
import os
import tempfile
from unittest.mock import patch
import torch
from torch.utils.data import DataLoader, TensorDataset
from accelerate import DistributedType, infer_auto_device_map, init_empty_weights
from accelerate.accelerator import Accelerator
from accelerate.state ... | 274 | 0 |
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
)
from .... | 417 | import argparse
import requests
import torch
from PIL import Image
from torchvision.transforms import Compose, Normalize, Resize, ToTensor
from transformers import SwinaSRConfig, SwinaSRForImageSuperResolution, SwinaSRImageProcessor
def __lowercase ( lowerCamelCase : str ):
UpperCamelCase_ : Any ... | 417 | 1 |
'''simple docstring'''
import unittest
from parameterized import parameterized
from transformers import AutoTokenizer, GPTNeoXConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...... | 708 |
'''simple docstring'''
import unittest
from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
UpperCamelCase : List[Any] = get_tests_dir('fi... | 9 | 0 |
import heapq
import sys
import numpy as np
A_ : Optional[int] =tuple[int, int]
class lowercase_ :
"""simple docstring"""
def __init__( self ):
"""simple docstring"""
a_ =... | 483 |
'''simple docstring'''
import argparse
import torch
from transformers import OpenAIGPTConfig, OpenAIGPTModel, load_tf_weights_in_openai_gpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def lowerCamelCase( SCREAMING_SNAKE_CASE_ ,SCREAMING_SNA... | 366 | 0 |
'''simple docstring'''
import sys
from typing import Tuple
import numpy as np
import torch
from PIL import Image
from torch import nn
from transformers.image_utils import PILImageResampling
from utils import img_tensorize
class __magic_name__ :
def __init__( self : str , ... | 708 | '''simple docstring'''
import pytest
import datasets.config
from datasets.utils.info_utils import is_small_dataset
@pytest.mark.parametrize("""dataset_size""" , [None, 400 * 2**20, 600 * 2**20] )
@pytest.mark.parametrize("""input_in_memory_max_size""" , ["""default""", 0, 100 * 2**20, 900 * 2**20] ... | 30 | 0 |
def lowerCAmelCase_ ( __a , __a ) -> float:
"""simple docstring"""
_validate_point(__a )
_validate_point(__a )
if len(__a ) != len(__a ):
raise ValueError("Both points must be in the same n-dimensional space" )
return float(sum(abs(a ... | 59 |
import pyarrow.parquet as pq
import pytest
from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size
from ..utils import assert_arrow_... | 59 | 1 |
'''simple docstring'''
import qiskit
def lowercase__ ( __UpperCamelCase = 2 )-> qiskit.result.counts.Counts:
UpperCamelCase = qubits
# Using Aer's simulator
UpperCamelCase = qiskit.Aer.get_backend("""aer_simulator""" ... | 711 |
'''simple docstring'''
import collections
import inspect
import unittest
from typing import Dict, List, Tuple
from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_availab... | 35 | 0 |
def SCREAMING_SNAKE_CASE ( lowercase_=28_123 ) -> Union[str, Any]:
"""simple docstring"""
A__ = [1] * (limit + 1)
for i in range(2 , int(limit**0.5 ) + 1 ):
sum_divs[i * i] += i
for k in range(i + 1 , limit // i + 1 ... | 87 |
"""simple docstring"""
from collections import OrderedDict
from typing import List, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase_ = logging.get_logger(__name__... | 560 | 0 |
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_ : Dict = False
class UpperCamelCase ( unittest.TestCase ):
def UpperCamelCase ( sel... | 673 |
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer
a_ : Optional[Any] = logging.get_logger(__name__)
a_ : Optional[Any] = {"vocab_file": "vocab.j... | 673 | 1 |
import json
import os
import unittest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
... | 556 |
from __future__ import annotations
def lowerCAmelCase_ (lowerCAmelCase__: int | str ):
"""simple docstring"""
UpperCAmelCase_: Optional[int] = str(lowerCAmelCase__ )
return n == n[::-1]
def lowerCAmelCase_ (lowerCAmelCase__: int = 1_... | 556 | 1 |
import re
from typing import Callable, List, Optional, Union
import tensorflow as tf
try:
from tensorflow.keras.optimizers.legacy import Adam
except ImportError:
from tensorflow.keras.optimizers import Adam
class __lowerCamelCase ( tf.keras.optimizers.schedules.LearningRateSchedule ... | 703 |
import inspect
import unittest
from transformers import MobileNetVaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
fro... | 166 | 0 |
"""simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_a : List[Any] = logging.get_logger(__name__)
_a : Dict = {
'microsoft/unispeech-sat-base-100h-libri-ft': (
'https://huggingface.co/m... | 213 | """simple docstring"""
import unittest
from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
_a : int = get_tests_dir('fixtures/test_sentencepiece_... | 213 | 1 |
import itertools
import json
import os
import unittest
from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...t... | 715 |
import torch
from diffusers import CMStochasticIterativeScheduler
from .test_schedulers import SchedulerCommonTest
class UpperCamelCase_ ( UpperCamelCase ):
lowercase = (CMStochasticIterativeScheduler,)
lowercase = 10
def snake_case__( ... | 307 | 0 |
"""simple docstring"""
import json
import os
import unittest
from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from ... | 560 |
"""simple docstring"""
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
class __A ( A_ ):
'''simple docstring'''
lowerCAmelCase : int ... | 560 | 1 |
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextModel,
CLIPTokenizer,
WhisperForConditionalGeneration,
WhisperProcessor,
)
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipelin... | 710 |
class _lowercase :
def __init__( self , a ):
snake_case__ : Optional[int] =size
snake_case__ : List[Any] =[0] * size
snake_case__ : List[Any] =[0] * size
@staticmethod
def lowercase__ ( a ):
... | 448 | 0 |
'''simple docstring'''
def a__ ( lowerCAmelCase__ ) -> list:
for i in range(len(lowerCAmelCase__ ) - 1 , 0 , -1 ):
UpperCAmelCase__ : Optional[int] = False
for j in range(lowerCAmelCase__ , 0 , -1 ):
if unsorted[j... | 75 |
'''simple docstring'''
import unittest
import numpy as np
import timeout_decorator # noqa
from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...tes... | 75 | 1 |
'''simple docstring'''
# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V an... | 187 |
'''simple docstring'''
import tensorflow as tf
from ...tf_utils import shape_list
class lowercase ( tf.keras.layers.Layer ):
'''simple docstring'''
def __init__( self : Union[str, Any] , __lowerCamelCase : str , __lowerCamelCase : in... | 187 | 1 |
"""simple docstring"""
import numpy
class _lowerCamelCase :
def __init__( self : Union[str, Any] , UpperCamelCase : numpy.ndarray , UpperCamelCase : numpy.ndarray ) -> Tuple:
"""simple docstring"""
lowerCAmelCase__ : ... | 299 |
import collections
import os
import re
from pathlib import Path
lowerCamelCase_ : Optional[Any] = """src/transformers"""
# Matches is_xxx_available()
lowerCamelCase_ : Union[str, Any] = re.compile(r"""is\_([a-z_]*)_available()""")
# Catches a one-line _import_struct = {xxx}
lowerCamelCase_ : i... | 548 | 0 |
'''simple docstring'''
from io import BytesIO
from typing import List, Union
import requests
from ..utils import add_end_docstrings, is_decord_available, is_torch_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_decord_available():
import numpy as np
fro... | 719 |
'''simple docstring'''
import json
import logging
import os
import socket
import git
import numpy as np
import torch
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO,
)
__a = logging.getLo... | 301 | 0 |
"""simple docstring"""
from typing import Optional
from urllib.parse import quote
import huggingface_hub as hfh
from packaging import version
def snake_case ( A__ ,A__ ,A__ = None ):
if version.parse(hfh.__version__ ).release < version.parse("0.11.0" ).release:
# old versions of h... | 95 |
from torch import nn
def SCREAMING_SNAKE_CASE__ ( UpperCamelCase__: str ):
if act_fn in ["swish", "silu"]:
return nn.SiLU()
elif act_fn == "mish":
return nn.Mish()
elif act_fn == "gelu":
return nn.GELU()
else:
raise ValueError(f'''Unsupported activation function:... | 6 | 0 |
'''simple docstring'''
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,
... | 705 |
'''simple docstring'''
import json
import os
import tempfile
import datasets
from utils import generate_example_dataset, get_duration
A__: Dict = 5_0000
A__: Optional[int] = 5000
A__ , A__: Optional[int] = os.path.split(__file__)
A__: ... | 506 | 0 |
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny model through reduction of a normal pre-trained model, but keeping the
# full vocab, merges file... | 475 |
import doctest
from collections import deque
import numpy as np
class __lowercase :
"""simple docstring"""
def __init__( self ) -> None:
snake_case : Any = [2, 1, 2, -1]
snake_case : int = [1, 2, 3, 4]
def... | 587 | 0 |
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(__na... | 154 |
from __future__ import annotations
import csv
import requests
from bsa import BeautifulSoup
def A ( a_ = "" ) -> dict[str, float]:
__UpperCamelCase : Tuple =url or 'https://www.imdb.com/chart/top/?ref_=nv_mv_250'
__UpperCamelCase : Optional[int... | 154 | 1 |
# 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 re... | 519 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCamelCase : int = {
'configuration_pegasus_x': ['PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PegasusXConfig'],
}
try:
if not is_torch_available():
r... | 519 | 1 |
import warnings
from ...utils import logging
from .image_processing_mobilevit import MobileViTImageProcessor
__a = logging.get_logger(__name__)
class A__ ( UpperCamelCase ):
"""simple docstring"""
def __init__( self : Dict , *lowerCAmelCase__ : Dict , **lower... | 702 | '''simple docstring'''
import argparse
import torch
from transformers import GPTaLMHeadModel, RobertaForMaskedLM
if __name__ == "__main__":
__a = argparse.ArgumentParser(
description=(
'Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transfe... | 257 | 0 |
import argparse
import json
import os
from tensorflow.core.protobuf.saved_model_pba import SavedModel
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
lowerCAmelCase__ = """."""
# Internal TensorFlow ops that can be sa... | 514 |
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: list ) -> list:
'''simple docstring'''
if len(SCREAMING_SNAKE_CASE_ ) <= 1:
return [tuple(SCREAMING_SNAKE_CASE_ )]
A__ = []
def generate(SCREAMING_SNAKE_CASE_: int , SCREAMING_SN... | 514 | 1 |
from __future__ import annotations
_lowercase : Dict =[-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0]
_lowercase : str =[-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1]
def __UpperCAmelCase ( UpperCamelCase__ :list[float] ) -> list[float]:
... | 718 |
'''simple docstring'''
import itertools
import string
from collections.abc import Generator, Iterable
def __UpperCAmelCase ( UpperCamelCase__ :Iterable[str] , UpperCamelCase__ :int ) -> Generator[tuple[str, ...], None, None]:
snake_case__ : Union[str, Any] ... | 574 | 0 |
'''simple docstring'''
import json
import os
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__lowerCamelCase : Optional[Any] = logging.get_logger(__name__)
__lowerCamelCase : List[Any] = ... | 501 |
'''simple docstring'''
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
from .tokenization_gpta import G... | 501 | 1 |
import argparse
import os
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_task_guides.py
_lowerCamelCase : List[Any] = """src/transformers"""
_lower... | 709 |
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_in... | 308 | 0 |
from typing import List
import datasets
from datasets.tasks import AudioClassification
from ..folder_based_builder import folder_based_builder
A_ : str = datasets.utils.logging.get_logger(__name__)
class _lowerCAmelCase( folder_based_builder.FolderBas... | 57 |
lowercase__ : Dict = [sum(int(c, 10) ** 2 for c in i.__str__()) for i in range(100_000)]
def SCREAMING_SNAKE_CASE ( __UpperCamelCase) -> int:
a = 0
while number:
# Increased Speed Slightly by checking every 5 digits together.
sum_of_digits_squared ... | 515 | 0 |
_A = 8.314_4598
def lowercase_ ( A__ , A__ ) -> float:
"""simple docstring"""
if temperature < 0:
raise Exception("Temperature cannot be less than 0 K" )
if molar_mass <= 0:
raise Exception("Molar mass cannot be less than or equal to 0 kg... | 294 |
from binascii import hexlify
from hashlib import shaaaa
from os import urandom
# RFC 3526 - More Modular Exponential (MODP) Diffie-Hellman groups for
# Internet Key Exchange (IKE) https://tools.ietf.org/html/rfc3526
_A = {
# 1536-bit
5: {
"prime": int(
"FFFFFFFFF... | 294 | 1 |
import logging
import os
import sys
import warnings
from dataclasses import dataclass, field
from random import randint
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import DatasetDict, load_dataset
import transformers
from transformers import (
... | 234 |
from __future__ import annotations
def a (_lowerCAmelCase ):
SCREAMING_SNAKE_CASE_ = len(_lowerCAmelCase ) // 2
# choose the middle 3 elements
SCREAMING_SNAKE_CASE_ = lst[m - 1 : m + 2]
# if middle element is peak
if three[1] > three[0... | 234 | 1 |
"""simple docstring"""
def lowercase_ ( _lowerCamelCase: int = 1 , _lowerCamelCase: int = 1000 ) -> Optional[int]:
'''simple docstring'''
__lowerCamelCase : Optional[Any] = 1
__lowerCamelCase : Dict = 0
for divide_by_number in range... | 711 | """simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
... | 366 | 0 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image
from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformer... | 50 |
'''simple docstring'''
from typing import Optional
import pyspark
from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader
class __lowercase (__lowerCamelCase ... | 596 | 0 |
'''simple docstring'''
from typing import Dict, Iterable, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
... | 707 |
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.alt_diffusion.modeling_roberta_series import (
... | 472 | 0 |
"""simple docstring"""
a_ = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ'
def __UpperCAmelCase ( ):
__lowercase : Any = input('''Enter message: ''' )
__lowercase : Union[str, Any] = input('''Enter key [alphanumeric]: ''' )
__lowercase ... | 76 |
"""simple docstring"""
import gc
import threading
import time
import psutil
import torch
class UpperCAmelCase_ :
def __init__( self ) -> str:
__lowercase : List[Any] = psutil.Process()
__lowercase : Any = False
def ... | 76 | 1 |
def lowerCamelCase_ ( _a : float , _a : float , _a : int ):
'''simple docstring'''
if principal <= 0:
raise Exception("""Principal borrowed must be > 0""" )
if rate_per_annum < 0:
raise Exception("""Rate of interest must be >= 0""" ... | 702 |
from typing import Callable, List, Optional, Tuple, Union
import torch
from transformers import CLIPTextModel, CLIPTokenizer
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin, TransformeraDModel, VQModel
from ...schedulers import VQDiffusionScheduler
from ...utils... | 322 | 0 |
import gc
import random
import unittest
import torch
from diffusers import (
IFImgaImgPipeline,
IFImgaImgSuperResolutionPipeline,
IFInpaintingPipeline,
IFInpaintingSuperResolutionPipeline,
IFPipeline,
IFSuperResolutionPipeline,
)
from diffusers.models.attention_processo... | 392 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
if is_torch_available():
import torch
from transformers import XLMRobertaModel
@require_sentencepiece
@require_tokeni... | 392 | 1 |
import importlib
import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel
def lowercase__ ( __A: str ,__A: int=False ):
'''simple docstring'''
__magic_name__ : List[Any] = OmegaConf.load(__A )
if displa... | 501 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_tf_available,
is_torch_available,
)
__lowerCamelCase : List[Any] = {
'''configuration_speech_to_text''': ['''SPEECH_TO_... | 501 | 1 |
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
_A : str = {"""configuration_gpt_neox""": ["""GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTNeoXConfig"""]}
try:
if not is_to... | 100 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__lowercase : List[str] = {"""configuration_van""": ["""VAN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """VanConfig"""]}
try:
if not i... | 142 | 0 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testi... | 704 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import _LazyModule
a : List[str] = {"""processing_wav2vec2_with_lm""": ["""Wav2Vec2ProcessorWithLM"""]}
if TYPE_CHECKING:
from .processing_wavaveca_with_lm import WavaVecaProcessorWithLM
... | 85 | 0 |
import torch
from diffusers import DiffusionPipeline
class lowerCamelCase_ ( _lowercase ):
def __init__( self : Optional[int] , __A : Optional[Any] , __A : Dict ):
super().__init__()
self.register_modules(unet=__A , scheduler=_... | 17 |
from __future__ import annotations
import unittest
from transformers import FunnelConfig, is_tf_available
from transformers.testing_utils import require_tf
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
f... | 542 | 0 |
from __future__ import annotations
from decimal import Decimal
from numpy import array
def __lowercase ( _UpperCAmelCase ) -> list[list[float]]:
'''simple docstring'''
__lowercase = Decimal
# Check if the provided matrix has 2 rows and 2 columns
# since this implementation only... | 576 | 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_xglm': ['XGLM_PRETRAINED_CONFIG_ARC... | 576 | 1 |
from maths.prime_factors import prime_factors
def SCREAMING_SNAKE_CASE_ ( _snake_case :int ) -> int:
if not isinstance(_snake_case , _snake_case ):
_A = F'''Input value of [number={number}] must be an integer'''
raise TypeError(_snake_case )
if number <... | 2 |
from __future__ import annotations
from sys import maxsize
from typing import Generic, TypeVar
UpperCAmelCase_ = TypeVar("""T""")
def SCREAMING_SNAKE_CASE_ ( _snake_case :int ) -> int:
return (position - 1) // 2
def SCREAMING_SNAKE_CASE_ ( _snake_case :int ) -> ... | 2 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a__: Optional[Any] = logging.get_logger(__name__)
a__: Tuple = {}
class SCREAMING_SNAKE_CASE__ ( UpperCamelCase__ ):
__SCREAMING_SNAKE_CASE = '''llama'''
__S... | 212 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a__: Any = logging.get_logger(__name__)
a__: List[str] = {
'microsoft/trocr-base-handwritten': (
'https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main/config.js... | 212 | 1 |
"""simple docstring"""
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
__lowerCAmelCase : ... | 58 |
"""simple docstring"""
from __future__ import absolute_import, division, print_function, unicode_literals
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers import RobertaConfig
from transformers.file_utils import add_start_docstrings, add_start_docstrings_t... | 58 | 1 |
'''simple docstring'''
from datetime import datetime
import matplotlib.pyplot as plt
import torch
def __snake_case ( lowercase : Union[str, Any] ):
for param in module.parameters():
snake_case_ = False
def __snake_case ( ):
snake_case_ = "cu... | 721 |
'''simple docstring'''
import json
import os
import re
import unicodedata
from json.encoder import INFINITY
from typing import Any, Dict, List, Optional, Tuple, Union
import numpy as np
import regex
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import Batch... | 420 | 0 |
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils import ... | 113 |
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from huggingface_hub import HfFolder, Repository, create_repo, delete_repo
from requests.exceptions import HTTPError
import transformers
from transformers import (
CONFIG_MAPPING,
FEATURE_EX... | 113 | 1 |
'''simple docstring'''
import bza
import gzip
import lzma
import os
import shutil
import struct
import tarfile
import warnings
import zipfile
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Dict, List, Optional, Type, Union
from .. import config
from .filelock i... | 714 |
'''simple docstring'''
import argparse
import glob
import logging
import os
import time
from argparse import Namespace
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from torch.utils.data import DataLoader, TensorDataset
from transfor... | 347 | 0 |
import contextlib
import csv
import json
import os
import sqlitea
import tarfile
import textwrap
import zipfile
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
import datasets
import datasets.config
@pytest.fixture(scope='session' )
def __lowerCAmelCase( ... | 27 |
"""simple docstring"""
import inspect
import os
import re
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/ch... | 554 | 0 |
def a__ ( snake_case ) -> bool:
"""simple docstring"""
if not isinstance(__lowerCAmelCase , __lowerCAmelCase ):
raise ValueError('''Input series is not valid, valid series - [2, 4, 6]''' )
if len(__lowerCAmelCase ) == 0:
raise ValueError('''Input l... | 712 |
from math import pi, sqrt
def a__ ( snake_case ):
"""simple docstring"""
if num <= 0:
raise ValueError('''math domain error''' )
if num > 171.5:
raise OverflowError('''math range error''' )
elif num - int(snake_case ) not in (0, 0.5):
raise NotImplemen... | 131 | 0 |
"""simple docstring"""
import os
from math import logaa
def lowercase__ ( snake_case_ :str = "base_exp.txt" ):
__UpperCAmelCase = 0
__UpperCAmelCase = 0
for i, line in enumerate(open(os.path.join(os.path.dirname(snake_case_ ) , snake_case_ ) ... | 49 |
"""simple docstring"""
from collections import deque
class _UpperCAmelCase :
def __init__( self : List[Any] , _lowercase : str , _lowercase : int , _lowercase : int ):
__UpperCAmelCase = process_name # process name
_... | 49 | 1 |
"""simple docstring"""
def __lowerCamelCase ( SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,) -> float:
"""simple docstring"""
_UpperCAmelCase = [redshift, radiation_density, matter_density, dark... | 494 |
"""simple docstring"""
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
from .timesteps import (
fastaa_timesteps,
smartaa_timestep... | 494 | 1 |
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/main/config.json',
}
class ... | 39 |
import warnings
from ...utils import logging
from .image_processing_deit import DeiTImageProcessor
_a : Optional[Any] = logging.get_logger(__name__)
class UpperCamelCase_ ( __UpperCamelCase ):
"""simple docstring"""
def __init__( self , *UpperCAmelCase... | 479 | 0 |
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel
from ...utils import logging
_lowercase = logging.get_logger(__name__)
def _A (UpperCamelCase : Dict , UpperCamelCase : Optional[int] ) ->Tuple:
'''simp... | 717 |
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel
from ...utils import logging
_lowercase = logging.get_logger(__name__)
def _A (UpperCamelCase : Dict , UpperCamelCase : Optional[int] ) ->Tuple:
'''simp... | 96 | 0 |
"""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_uti... | 95 |
def lowercase_( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
if a < 0 or b < 0:
raise ValueError("the value of both inputs must be positive" )
lowerCamelCase : List[str] = str(bin(SCREAMING_SNAKE_CASE_ ) )[2:] # remo... | 340 | 0 |
'''simple docstring'''
import unittest
from datasets import load_dataset
from transformers.pipelines import pipeline
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_torch, slow
@is_pipeline_test
@require_torch
class _lowerCamelCase ( unittest.TestCase ):
'''s... | 540 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from diffusers import (
AudioDiffusionPipeline,
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
DiffusionPipeline,
Mel,
UNetaDConditionModel,
UNetaDModel,
)
from diffusers.utils import slow, torch_device
f... | 540 | 1 |
'''simple docstring'''
from torch import nn
class __lowerCAmelCase ( nn.Module ):
def __init__(self , lowerCAmelCase__ , lowerCAmelCase__ ):
super().__init__()
_UpperCAmelCase : Union[str, Any] = class_size
... | 414 |
'''simple docstring'''
from __future__ import annotations
from random import random
class __lowerCAmelCase :
def __init__(self , lowerCAmelCase__ = None ):
_UpperCAmelCase : List[Any] = value
_UpperCAmelCase : Optional... | 414 | 1 |
a__: int = '\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://github.com/huggingface/transfo... | 718 |
import warnings
from ...utils import logging
from .image_processing_dpt import DPTImageProcessor
a__: Optional[int] = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE__ ( UpperCamelCase__ ):
def __init__( self,*__lowerCamelCase,**__lowerCamelCa... | 212 | 0 |
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transforme... | 27 |
# Copyright 2022 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 r... | 27 | 1 |
'''simple docstring'''
import unittest
from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
UpperCamelCase_... | 178 |
'''simple docstring'''
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
UpperCamelCase__ : Tuple = logging.get_logger(__name__)
UpperCamelCase__ : List[str] = {
'''t5-... | 178 | 1 |
from collections import namedtuple
import requests
from lxml import html # type: ignore
A_: List[str] = namedtuple('covid_data', 'cases deaths recovered')
def __lowerCAmelCase ( _A = "https://www.worldometers.info/coronavirus/" ):
"""simple docstring"""
_lowercase ... | 398 |
'''simple docstring'''
from diffusers.utils.testing_utils import require_onnxruntime
@require_onnxruntime
class _SCREAMING_SNAKE_CASE :
"""simple docstring"""
pass
| 316 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase = logging.get_logger(__name__)
_lowercase = {
'tanreinama/GPTSAN-2.8B-spout_is_uniform': (
'https://huggingface.co/tanreinama/G... | 44 |
'''simple docstring'''
from collections import Counter
from pathlib import Path
from typing import Optional, Tuple
import yaml
class _lowercase ( yaml.SafeLoader ):
def UpperCamelCase ( self , A__ ) -> List[str]:
snake_case =... | 44 | 1 |
'''simple docstring'''
import re
def _SCREAMING_SNAKE_CASE (A ) -> bool:
"""simple docstring"""
lowercase__ = re.compile(R'''^(\+91[\-\s]?)?[0]?(91)?[789]\d{9}$''' )
if match := re.search(A , A ):
return match.string == phone
return F... | 460 |
'''simple docstring'''
import argparse
import json
import os
from collections import OrderedDict
import numpy as np
import tensorflow as tf
import torch
def _SCREAMING_SNAKE_CASE (A ) -> Dict:
"""simple docstring"""
lowercase__ = os.path.join(args.tf_model_dir , ''... | 460 | 1 |
"""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
__lowercase : List[str] = logging.get_logger(__name__)
__lowercase : Union[str, ... | 93 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__lowercase : Dict = {
"configuration_vision_text_dual_encoder": ["Visi... | 93 | 1 |
"""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... | 96 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
from multiprocessing import get_context
from pathlib import Path
import datasets
import numpy as np
from datasets import load_dataset
from parameterized import parameterized
from transforme... | 96 | 1 |
"""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,
get_resize_output_image_size,
normalize,
rescale,
re... | 93 | """simple docstring"""
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from ... | 93 | 1 |
from argparse import ArgumentParser, Namespace
from typing import Any, List, Optional
from ..pipelines import Pipeline, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand
try:
from fastapi import Body, FastAPI, HTTPException
from fastapi.r... | 625 |
from __future__ import annotations
import requests
UpperCamelCase_ = set(
'approved_at_utc approved_by author_flair_background_color\nauthor_flair_css_class author_flair_richtext author_flair_template_id author_fullname\nauthor_premium can_mod_post category clicked content_categories c... | 625 | 1 |
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_lowerCamelCase = logging.get_logger(__name__)
_lowerCamelCase ... | 709 |
# 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 appli... | 447 | 0 |
from __future__ import annotations
UpperCamelCase = 8.9_88e9 # units = N * m^s * C^-2
def __lowerCamelCase ( __lowerCAmelCase : float , __lowerCAmelCase : float , __lowerCAmelCase : float , __lowerCAmelCase : f... | 269 |
from . import (
albert,
align,
altclip,
audio_spectrogram_transformer,
auto,
autoformer,
bark,
bart,
barthez,
bartpho,
beit,
bert,
bert_generation,
bert_japanese,
bertweet,
big_bird,
bigbird_pegasus,
biogpt,
bit,
blenderbot,
blen... | 269 | 1 |
'''simple docstring'''
__UpperCamelCase : Tuple = """0.18.2"""
from .configuration_utils import ConfigMixin
from .utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_inflect_available,
is_invisible_watermark_available,
is_k_diffu... | 270 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_tf_available,
is_torch_available,
)
__UpperCamelCase : Optional[int] ... | 270 | 1 |
def _UpperCAmelCase ( a : int ):
if num <= 0:
raise ValueError("""Input must be a positive integer""" )
snake_case__ = [True] * (num + 1)
snake_case__ = 2
while p * p <= num:
if primes[p]:
for... | 654 |
import tempfile
import torch
from diffusers import IPNDMScheduler
from .test_schedulers import SchedulerCommonTest
class _lowerCAmelCase ( lowercase_ ):
"""simple docstring"""
_lowercase : int = (IPNDMScheduler,)
_lowercase : int = (('''num_inference_steps''', 50... | 654 | 1 |
"""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,
get_resize_output_image_size,
normalize,
rescale,
resize,
to... | 711 |
"""simple docstring"""
import math
import qiskit
def __snake_case ( UpperCamelCase = 1 , UpperCamelCase = 1 , UpperCamelCase = 1 ) -> qiskit.result.counts.Counts:
"""simple docstring"""
if (
isinstance(UpperCamelCase , UpperCamelCase )
or isinstance(UpperCam... | 158 | 0 |
'''simple docstring'''
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__ : List[str] = logging.get_logger(__name__)
UpperCamelCase__ : Any = {
"microsoft/wavlm-base": "https://huggingface.co/microsoft/... | 591 |
import argparse
import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers import (
CLIPTokenizer,
CLIPTokenizerFast,
VideoMAEImageProcessor,
XCLIPConfig,
XCLIPModel,
XCLIPProcessor,
XCLIPTextConfig,
XCLIPVisionConfig,
)
def a__... | 74 | 0 |
__A : Any ={
"Pillow": "Pillow",
"accelerate": "accelerate>=0.11.0",
"compel": "compel==0.1.8",
"black": "black~=23.1",
"datasets": "datasets",
"filelock": "filelock",
"flax": "flax>=0.4.1",
"hf-doc-builder": "hf-doc-builder>=0.3.0",
"huggingface-hub": ... | 708 |
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny model through reduction of a normal pre-trained model, but keeping the
# full vocab, merges fi... | 241 | 0 |
'''simple docstring'''
import sys
import webbrowser
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
print('Googling.....')
lowerCAmelCase : List[Any] = 'https://www.google.com/search?q=' + ' '.join(sys.argv[1:])
... | 3 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase : Union[str, Any] = {
'configuration_git': ['GIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GitConfig', 'GitVisionConfig'],
'processing_... | 3 | 1 |
import argparse
import glob
import logging
import os
import sys
import time
from collections import defaultdict
from pathlib import Path
from typing import Dict, List, Tuple
import numpy as np
import pytorch_lightning as pl
import torch
from callbacks import SeqaSeqLoggingCallback, get_checkpoin... | 336 |
import math
import random
def lowerCAmelCase ( UpperCAmelCase, UpperCAmelCase = False ) ->float:
"""simple docstring"""
if deriv:
return value * (1 - value)
return 1 / (1 + math.exp(-value ))
# Initial Value
lowercase... | 336 | 1 |
import math
from datetime import datetime, timedelta
def lowerCAmelCase_ ( _SCREAMING_SNAKE_CASE : int ):
UpperCamelCase_ : Tuple = year % 19
UpperCamelCase_ : Optional[int] = year % 4
UpperCamelCase_ : int = year % 7
UpperCamelCase_ ... | 635 |
import warnings
from ...utils import logging
from .image_processing_mobilevit import MobileViTImageProcessor
lowerCamelCase = logging.get_logger(__name__)
class A ( UpperCamelCase_ ):
def __init__( self : Optional[Any] , *lowercase_ : str , **lowercase_ : List[Any] ) ... | 464 | 0 |
import gc
import unittest
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
PriorTransformer,
StableUnCLIPPipeline,
UNetaDConditionModel,
)
from diffusers.p... | 199 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...util... | 199 | 1 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@dataclass
class Up... | 5 |
"""simple docstring"""
import argparse
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import ... | 123 | 0 |
'''simple docstring'''
import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
d... | 705 |
__magic_name__ : List[str] = tuple[float, float, float]
__magic_name__ : Optional[int] = tuple[float, float, float]
def lowercase__ ( _UpperCamelCase , _UpperCamelCase) -> Vectorad:
"""simple docstring"""
UpperCamelC... | 410 | 0 |
def _a ( lowerCamelCase ):
return [
{
0: [1, 2],
1: [0, 2],
2: [0, 1, 3, 5],
3: [2, 4],
4: [3],
5: [2, 6, 8],
6: [5, 7],
7: [6, 8],
8: [5, 7],
},
{
0: [6],
1: [9],
2: [4, 5],
... | 681 |
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny model through reduction of a normal pre-trained model, but keeping the
# full vocab, merge... | 606 | 0 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE ( lowercase_ : str , lowercase_ : str ):
def get_matched_characters(lowercase_ : str , lowercase_ : str ) -> str:
lowercase = []
lowercase = min(len(_stra... | 653 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE ( lowercase_ : int , lowercase_ : int , lowercase_ : list[list[int]] ):
def update_area_of_max_square(lowercase_ : int , lowercase_ : int ) -> int:
# BASE CASE
if row >= r... | 653 | 1 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffu... | 361 |
"""simple docstring"""
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
if is_torch_available():
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
@requir... | 361 | 1 |
'''simple docstring'''
import importlib
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
import transformers.models.auto
from transformers.models.auto.configuration_auto import CONFIG_MAPPING, AutoConfig
from transform... | 666 |
'''simple docstring'''
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Image
from .base import TaskTemplate
@dataclass(frozen=SCREAMING_SNAKE_CASE_ )
class SCREAMING_SNAKE_CASE (... | 666 | 1 |
import argparse
import json
import os
from collections import OrderedDict
import numpy as np
import tensorflow as tf
import torch
def lowerCAmelCase_ ( __a ) -> int:
"""simple docstring"""
lowerCamelCase__: Any =os.path.join(args.tf_model_dir , "parameters... | 59 |
import math
import time
from typing import Dict, List, Optional
from torch.utils.data import Dataset
from transformers import SeqaSeqTrainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
import torch_... | 43 | 0 |
def __SCREAMING_SNAKE_CASE ( lowercase_ ) -> str:
"""simple docstring"""
__UpperCamelCase = len(a_ )
for i in range(length - 1 ):
__UpperCamelCase = i
for k in range(i + 1 , a_ ):
if collection[k] < collection[leas... | 705 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
"RWKV/rwkv-4-169m-pile": "https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json",
"RWKV/rwkv-4-430m-pile": "https://huggingface.co/RWKV/rwkv-4-430m-pi... | 375 | 0 |
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