code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
'''simple docstring'''
import inspect
import unittest
from transformers import YolosConfig
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_c... | 319 |
# 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 vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared to... | 278 | 0 |
"""simple docstring"""
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, ClassLabel, Features
from .base import TaskTemplate
@dataclass(frozen=_snake_case )
class __lowerCAmelCase ( _snake_case ):
'''simple docstring... | 362 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
lowercase_ = {
"configuration_vision_text_dual_encoder": ["VisionTextDual... | 11 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase_ = logging.get_logger(__name__)
UpperCamelCase_ = {
'''microsoft/markuplm-base''': '''https://huggingface.co/microsoft/markuplm-base/resolve/main/config.json''',
'''microsoft/markuplm... | 345 |
import unittest
from queue import Empty
from threading import Thread
from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device
from ..test_modeling_common import ids_tensor
if is_torch_availabl... | 345 | 1 |
'''simple docstring'''
import itertools
import string
from collections.abc import Generator, Iterable
def __UpperCAmelCase ( a_: Iterable[str], a_: int ):
_UpperCAmelCase : List[str] = iter(a_ )
while True:
_UpperCAmelCase : int = ... | 357 | '''simple docstring'''
def __UpperCAmelCase ( a_: int, a_: int ):
if not isinstance(a_, a_ ):
raise ValueError("iterations must be defined as integers" )
if not isinstance(a_, a_ ) or not number >= 1:
raise ValueError(
"starting number must be\n ... | 17 | 0 |
"""simple docstring"""
import warnings
from ..trainer import Trainer
from ..utils import logging
SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
class UpperCAmelCase_ ( A_ ):
def __init__( self : str , snake_case_ : List[Any]=None , **snake_case_... | 247 |
"""simple docstring"""
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
import numpy as np
# Parrameters
SCREAMING_SNAKE_CASE = (720, 1280) # Height, Width
SCREAMING_SNAKE_CASE = (0.4, 0.6) # if height or width lower than this scale, drop it.
SCREAM... | 247 | 1 |
import argparse
import os
import torch
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
_A : List[str] = {
'sample_size': 32,
'in_channels': 3,
'out_channels': 3,
'layers_per_block': 2,
'num_class_embeds': 10_00,
'block... | 265 |
def _a ( UpperCAmelCase ) -> int:
"""simple docstring"""
if not isinstance(UpperCAmelCase , UpperCAmelCase ) or number < 0:
raise ValueError('''Input must be a non-negative integer''' )
lowerCamelCase__ : List[str] = 0
while number:... | 265 | 1 |
import argparse
import re
import requests
import torch
# git clone https://github.com/salesforce/BLIP.git
from models.blip import blip_decoder
from models.blip_itm import blip_itm
from models.blip_vqa import blip_vqa
from PIL import Image
from torchvision import transforms
from torchvision.transforms.functional i... | 82 | """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 loggin... | 172 | 0 |
'''simple docstring'''
import os
# Precomputes a list of the 100 first triangular numbers
__lowerCAmelCase = [int(0.5 * n * (n + 1)) for n in range(1, 101)]
def __lowerCamelCase ( ) -> int:
_a : Union[str, Any] = os.path.dirname(os.path.realpath(_A ) )
... | 353 |
'''simple docstring'''
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 ...tes... | 107 | 0 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_chinese_clip import ChineseCLIPImageProcessor
_A : List[str] = logging.get_logger(__name__)
class _lowercase ( UpperCAmelCase__ ):
'''simple docstring'''
... | 229 | '''simple docstring'''
import argparse
import torch
from torch import nn
from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def UpperCamelCase_ ( snake_case_ : Any ) -> Optional[Any]:
'''simple docstring'''
__lowerCAmel... | 229 | 1 |
'''simple docstring'''
import torch
from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde
from .test_schedulers import SchedulerCommonTest
@require_torchsde
class _snake_case (_lowerCAmelCase):
__A ... | 365 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class _snake_case (metaclass=__SCREAMING_SNAKE_CASE):
__A : Union[str, Any] =["torch", "torchsde"]
def __init__( self ,*_snake_case ,**_snake_case ):
requires_backends(self ,["torc... | 67 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections import namedtuple
from dataclasses import dataclass
@dataclass
class A_ :
_lowerCamelCase : int
_lowerCamelCase : TreeNode | None = None
_lowerCamelCase : TreeNode | None = None
__SC... | 22 |
'''simple docstring'''
import re
from filelock import FileLock
try:
import nltk
__SCREAMING_SNAKE_CASE :Optional[int] = True
except (ImportError, ModuleNotFoundError):
__SCREAMING_SNAKE_CASE :str = False
if NLTK_AVAILABLE:
with FileLock('''.lock''') as lock:
nltk.dow... | 22 | 1 |
'''simple docstring'''
class UpperCamelCase__ :
"""simple docstring"""
def __init__( self , snake_case__ ):
'''simple docstring'''
_lowerCAmelCase : List[Any] = len(lowerCAmelCase_ )
_lowerCAmelCase : List[str... | 364 |
'''simple docstring'''
def lowercase (_A = 1_0_0_0_0_0_0 ):
"""simple docstring"""
_lowerCAmelCase : Any = set(range(3 , _A , 2 ) )
primes.add(2 )
for p in range(3 , _A , 2 ):
... | 25 | 0 |
import tempfile
import unittest
import numpy as np
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_flax
if is_flax_available():
imp... | 116 | import unittest
from transformers import SqueezeBertConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, ran... | 18 | 0 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE_ ( __A : float , __A : float , __A : float ) -> dict[str, float]:
"""simple docstring"""
if (voltage, current, resistance).count(0 ) != 1:
raise ValueError... | 120 |
def SCREAMING_SNAKE_CASE_ ( __A : int ) -> int:
"""simple docstring"""
if not isinstance(__A , __A ):
raise ValueError('Input must be an integer' )
if input_num <= 0:
raise ValueError('Input must be positive' )
... | 120 | 1 |
def _a ( a :int = 10 , a :int = 1_000 , a :bool = True ) -> int:
assert (
isinstance(UpperCamelCase__ , UpperCamelCase__ )
and isinstance(UpperCamelCase__ , UpperCamelCase__ )
and isinstance(UpperCamelCase__ , UpperCamelCase__ )
), ... | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_speech_available, is_torch_available
__A = {
"configuration_audio_spectrogram_transformer": [
"AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"ASTConfig... | 90 | 0 |
'''simple docstring'''
import shutil
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import (
is_pt_tf_cross_test,
require_tf,
require_torch,
require_torchvision,
require_vision,
)
from transformers.utils import is_tf_available, is_torch_available, is_vision_a... | 360 |
'''simple docstring'''
from __future__ import annotations
def __magic_name__ ( A ) -> None:
create_state_space_tree(A , [] , 0 , [0 for i in range(len(A ) )] )
def __magic_name__ ( A , A , A , A , ) -> None:
if index ... | 332 | 0 |
# 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 required ... | 312 |
import unittest
from typing import Tuple
import torch
from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch
@require_torch
class _a :
"""simple docstring"""
@property
def __A ... | 312 | 1 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available... | 362 |
# 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
#
# Unl... | 39 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = {
'facebook/s2t-wav2vec2-large-en-de': (
'https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/config.json'
),
# ... | 12 |
snake_case : str = '''
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
'''
snake_case : List[... | 94 | 0 |
'''simple docstring'''
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class... | 358 |
'''simple docstring'''
import argparse
import numpy as np
import torch
from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging
logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__ = logging.get_logger('transformers.models.speecht5')
def lowercase... | 183 | 0 |
'''simple docstring'''
def UpperCamelCase_( snake_case : float , snake_case : float ):
'''simple docstring'''
if density <= 0:
raise ValueError("Impossible fluid density" )
if bulk_modulus <= 0:
raise ValueEr... | 85 |
'''simple docstring'''
import argparse
import json
from typing import List
from ltp import LTP
from transformers.models.bert.tokenization_bert import BertTokenizer
def UpperCamelCase_( snake_case : Any ):
'''simple docstring'''
if (
(cp >= 0X4E00 and cp... | 85 | 1 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_... | 336 |
"""simple docstring"""
import inspect
import re
from hashlib import shaaaa
from typing import Dict, List
from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from... | 336 | 1 |
import argparse
from torch import nn
# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
ProphetNetForConditionalGeneration as Prophet... | 270 |
from collections import deque
from .hash_table import HashTable
class __lowerCAmelCase ( lowerCAmelCase_ ):
"""simple docstring"""
def __init__( self : Union[str, Any] , *_snake_case : Union[str, Any] , **_snake_case : Union[str, Any]... | 156 | 0 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Callable
from typing import Any, Generic, TypeVar
_lowerCAmelCase : Optional[int] = TypeVar("""T""")
class lowerCAmelCase__ ( Generic[T] ):
def __init__( self : List[str] , snake_c... | 298 |
"""simple docstring"""
import functools
def SCREAMING_SNAKE_CASE__ ( snake_case : str , snake_case : str )-> int:
'''simple docstring'''
UpperCAmelCase__ : List[str] = len(snake_case )
UpperCAmelCase__ : str = len(snake_case ... | 298 | 1 |
"""simple docstring"""
import json
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common impo... | 332 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision import transforms
from transformers import BitImageProcessor, FocalNetConfig, FocalNetForImageClassification
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAG... | 278 | 0 |
from ....utils import logging
_A = logging.get_logger(__name__)
class UpperCAmelCase__ ( A_ ):
"""simple docstring"""
def __init__( self , A_ , A_=None , A_=2048 ) -> Any:
__UpperCamelCase =config.__dict__
__UpperCamelCase ... | 353 |
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.transforms.functional import Inter... | 117 | 0 |
'''simple docstring'''
from .glue import glue_convert_examples_to_features, glue_output_modes, glue_processors, glue_tasks_num_labels
from .squad import SquadExample, SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
from .utils import DataProcessor, InputExample, InputFe... | 304 |
'''simple docstring'''
from __future__ import annotations
import math
def __UpperCAmelCase ( A : int , A : int , A : bool , A : list[int] , A : float ) -> int:
if depth < 0:
raise ValueError('''Depth cannot be less th... | 304 | 1 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils impo... | 115 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
SCREAMING_SNAKE_CASE_:Any = {
"""configuration_mobilenet_v2""": [
"""MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""MobileNetV2Config""",
... | 115 | 1 |
lowerCamelCase : List[Any] = 9.8_0665
def _SCREAMING_SNAKE_CASE ( lowercase : float , lowercase : float , lowercase : float = g ):
'''simple docstring'''
if fluid_density <= 0:
raise ValueError('Impossible fluid den... | 204 |
from itertools import zip_longest
import requests
from bsa import BeautifulSoup
from pandas import DataFrame
def _SCREAMING_SNAKE_CASE ( lowercase : str = "laptop" ):
'''simple docstring'''
lowerCamelCase_ = f"""https://www.amazon.in/laptop/s?k={pr... | 204 | 1 |
import string
def A__ ( lowerCamelCase ) -> None:
for key in range(len(string.ascii_uppercase ) ):
UpperCamelCase_: List[Any] = """"""
for symbol in message:
if symbol in string.ascii_uppercase:
UpperCamelCase_: List[str] = string.as... | 223 |
def A__ ( lowerCamelCase ) -> list:
return [
txt[:a] + txt[a].upper() + txt[a + 1 :]
for a in range(len(lowerCamelCase ) )
if txt[a].isalpha()
]
if __name__ == "__main__":
__import__("""doctest""").testmod()
| 223 | 1 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, TensorType
__UpperCAmelCas... | 29 |
"""simple docstring"""
import math_equivalence # From: git+https://github.com/hendrycks/math.git
import datasets
a :str = "\\n@article{hendrycksmath2021,\n title={Measuring Mathematical Problem Solving With the MATH Dataset},\n author={Dan Hendrycks\n and Collin Burns\n and Saurav Kadavath\n ... | 132 | 0 |
"""simple docstring"""
lowercase__ = {str(digit): digit**5 for digit in range(10)}
def __lowerCamelCase ( __UpperCamelCase ) -> int:
"""simple docstring"""
return sum(DIGITS_FIFTH_POWER[digit] for digit in str(__UpperCamelCase ) )
def __lowerCamelCase ( ) -> in... | 161 |
"""simple docstring"""
class __lowerCamelCase ( A__ ):
'''simple docstring'''
pass
class __lowerCamelCase ( A__ ):
'''simple docstring'''
pass
class __lowerCamelCase :
'''simple docstring'''
def __init__( self : ... | 161 | 1 |
from __future__ import annotations
def __UpperCamelCase ( _A : List[Any] , _A : List[str] = None , _A : int = None , _A : Dict = False , ) ->tuple[int, float, str]:
"""simple docstring"""
lowerCamelCase_ =cipher_alphabet or [chr(__UpperCamelCase ) for ... | 154 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
lowercase__ = {
"""configuration_clip"""... | 241 | 0 |
import requests
from bsa import BeautifulSoup
def UpperCamelCase_( snake_case__: str = "AAPL" ) -> str:
UpperCAmelCase__ = f"https://in.finance.yahoo.com/quote/{symbol}?s={symbol}"
UpperCAmelCase__ = BeautifulSoup(requests.get(snake_case__ ).text , 'html.pa... | 351 |
from multiprocessing import Lock, Pipe, Process
# lock used to ensure that two processes do not access a pipe at the same time
_UpperCamelCase = Lock()
def UpperCamelCase_( snake_case__: Optional[Any] , snake_case__: Optional[int] , snake_case__: Tuple , snake_case__: Tuple ... | 335 | 0 |
'''simple docstring'''
import unittest
from transformers import TrOCRConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import Mod... | 97 |
'''simple docstring'''
def a ( __a , __a ) -> int:
'''simple docstring'''
if len(__a ) != len(__a ):
raise ValueError('''String lengths must match!''' )
UpperCamelCase__ :Union[str, Any] = 0
for chara, chara in zip(__a , __a ):
... | 97 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase__ :Dict = {
"configuration_luke": ["LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP", "LukeConfig"],
"tokenization_luke": ["LukeTokenizer"],
}
try:
if not is_torch_available():
... | 97 |
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 import _PACKAGED_DATASETS_M... | 97 | 1 |
from math import isqrt
def UpperCAmelCase ( lowercase ):
"""simple docstring"""
return all(number % divisor != 0 for divisor in range(2 , isqrt(lowercase ) + 1 ) )
def UpperCAmelCase ( lowercase = 10**6 ):
"""simple doc... | 210 | import requests
def UpperCAmelCase ( lowercase , lowercase ):
"""simple docstring"""
__lowercase = {'''Content-Type''': '''application/json'''}
__lowercase = requests.post(lowercase , json={'''text''': message_body} , headers=lowerca... | 210 | 1 |
import math
from typing import Optional
import numpy as np
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase : Any = logging.get_logger(__name__)
_lowerCamelCase : Optional[int] = {
"facebook/encodec_24khz": "https://huggingface.... | 371 |
import random
class SCREAMING_SNAKE_CASE__ :
'''simple docstring'''
@staticmethod
def A ( lowercase : str ):
'''simple docstring'''
_snake_case = [ord(lowercase ) for i in text]
_snake_case = []
_snake_case = ... | 130 | 0 |
class _SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__(self : Any , UpperCAmelCase_ : str = "" , UpperCAmelCase_ : bool = False) ->None:
'''simple docstring'''
lowerCamelCase__: dict[str, RadixNode] ={}
# A node will be a leaf ... | 10 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import ... | 10 | 1 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import DebertaVaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTester... | 354 |
"""simple docstring"""
from .imports import is_tqdm_available
if is_tqdm_available():
from tqdm.auto import tqdm as _tqdm
from ..state import PartialState
def _snake_case ( lowercase__ : bool = True , *lowercase__ : Optional[int] , **lowercase__ : s... | 1 | 0 |
from __future__ import annotations
import unittest
import numpy as np
from transformers import LayoutLMConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_... | 287 |
import warnings
from ...utils import logging
from .image_processing_owlvit import OwlViTImageProcessor
_lowerCamelCase =logging.get_logger(__name__)
class A__ ( __SCREAMING_SNAKE_CASE):
def __init__( self , *__magic_name__ , **__magic_name__ ):
warnings.warn(
... | 287 | 1 |
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_tokenizers
@require_torch
cl... | 365 |
from ....utils import logging
_A = logging.get_logger(__name__)
class UpperCAmelCase__ ( A_ ):
"""simple docstring"""
def __init__( self , A_ , A_=None , A_=2048 ) -> Any:
__UpperCamelCase =config.__dict__
__UpperCamelCase ... | 117 | 0 |
"""simple docstring"""
import argparse
import json
import logging
import os
import shutil
import sys
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.utils import write_basic_config
from transformers.testing_utils import TestCasePlus, get_gpu_count, run_comman... | 126 |
from ...utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_torch_available,
is_transformers_available,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
fro... | 159 | 0 |
'''simple docstring'''
import json
import os
from typing import Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__lowerCAmelCase = logging.get_logger(__name__)
__lowerCAmelCase = {'''vocab_file''': '''vocab.json'''}
__lowerCAmelCas... | 369 |
'''simple docstring'''
import argparse
import re
from pathlib import Path
import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor
from transformers import (
EfficientFormerConfig,
EfficientFormerForImageClassificationWithTeach... | 107 | 0 |
import os
import re
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_UpperCAmelCase : Optional[int] = logging.get_logger(__name__)
_UpperCAmelCase : List[str] = {
"""vocab_... | 50 |
'''simple docstring'''
import sys
import webbrowser
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
print('''Googling.....''')
lowerCAmelCase : str ='''https://www.google.com/search?q=''' + ''' '''.join(sys.ar... | 223 | 0 |
"""simple docstring"""
def lowerCAmelCase (__UpperCamelCase : int = 5_0 ):
"""simple docstring"""
__UpperCamelCase =[1] * (length + 1)
for row_length in range(3 , length + 1 ):
for block_length in range(3 , row_length + 1 ):
for ... | 366 | """simple docstring"""
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
... | 85 | 0 |
'''simple docstring'''
from typing import List, Optional
import numpy as np
from ...processing_utils import ProcessorMixin
from ...utils import to_numpy
class a__( lowerCAmelCase__ ):
'''simple docstring'''
UpperCAmelCase_ : str = '''EncodecFeatureExtractor'''
Upper... | 272 | '''simple docstring'''
def snake_case__ ( _A: str ) -> list[int]:
'''simple docstring'''
lowerCAmelCase = [0 for i in range(len(_A ) )]
# initialize interval's left pointer and right pointer
lowerCAmelCase , lowerCAmelCase = 0, 0
for i in range(1 , le... | 272 | 1 |
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_timesteps,
smartaa_timesteps,
s... | 121 |
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import TimesformerConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils impo... | 121 | 1 |
'''simple docstring'''
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except Option... | 139 |
'''simple docstring'''
def A_ ( snake_case ):
SCREAMING_SNAKE_CASE:Any = len(snake_case )
for _ in range(snake_case ):
for i in range(_ % 2 , arr_size - 1 , 2 ):
if arr[i + 1] < arr[i]:
SCREAMING_SNAKE_CASE , SCREAMIN... | 139 | 1 |
"""simple docstring"""
import torch
from transformers import CamembertForMaskedLM, CamembertTokenizer
def lowercase_ ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase=5 ) -> Union[str, Any]:
# Adapted from https://github.com/pytorch/fairseq/blob/ma... | 354 |
"""simple docstring"""
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class _lo... | 212 | 0 |
from typing import List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase_ : Tuple = logging.get_logger(__name__)
lowercase_ : List[Any] = {
'huggingface/autoformer-tourism-monthly': 'https://huggingface.co/huggingface/autoformer-tourism-mo... | 133 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase_ : Dict = logging.get_logger(__name__)
lowercase_ : Union[str, Any] = {'ctrl': 'https://huggingface.co/ctrl/resolve/main/config.json'}
class __lowerCAmelCase ( UpperCAmelCase__ ):
snake_... | 133 | 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,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteSchedul... | 175 |
"""simple docstring"""
import json
import os
import unittest
from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
VOCAB_FILES_NAMES,
BlenderbotSmallTokenizer,
)
from ...test_tokenization_common import TokenizerTesterMixin
class __snake_case ( _lowercase , ... | 175 | 1 |
import warnings
from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline # noqa F401
warnings.warn(
'''The `inpainting.py` script is outdated. Please use directly `from diffusers import'''
''' StableDiffusionInpaintPipeline` instead.'''
)
| 122 |
import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path
import timm
import torch
from huggingface_hub import hf_hub_download
from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImageProcessor
from transformers.utils import ... | 122 | 1 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
UniSpeechConfig,
UniSpeechForCTC,
UniSpeechForPreTraining,
WavaVecaFeatureExtractor,
WavaVecaPhonemeCTCTokenizer,
WavaVecaProcessor,
logging,
)
logging.s... | 45 |
import argparse
import pytorch_lightning as pl
import torch
from torch import nn
from transformers import LongformerForQuestionAnswering, LongformerModel
class lowerCAmelCase ( pl.LightningModule ):
def __init__( self : List[str] , UpperCAmelCase : Optional[Any] ) ... | 45 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
snake_case_ = logging.get_logger(__name__)
snake_case_ = {
"""microsoft/focalnet-tiny""": """https://h... | 24 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SwiftFormerConfig,
SwiftFormerForImageClassification,
ViTImageProcessor,
)
from transformers.utils i... | 265 | 0 |
UpperCamelCase__ = tuple[float, float, float]
UpperCamelCase__ = tuple[float, float, float]
def _a ( SCREAMING_SNAKE_CASE_ : Pointad , SCREAMING_SNAKE_CASE_ : Pointad ):
__lowerCAmelCase = end_pointa[0] - end_pointa[0]
__... | 102 |
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
UpperCamelCase__ = """\
"""
UpperCamelCase__ = """
Perplexity (PPL) is one of the most common metrics for ev... | 102 | 1 |
'''simple docstring'''
from ...utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_torch_available,
is_transformers_available,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except O... | 319 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import SegformerImageProcessor, SwinConfig, UperNetConfig, UperNetForSemanticSegmentation
def __UpperCamelCase ( _A ):
lowerCAmelCase_ = 384
... | 278 | 0 |
import json
import os
import unittest
from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
VOCAB_FILES_NAMES,
BlenderbotSmallTokenizer,
)
from ...test_tokenization_common import TokenizerTesterMixin
class __lowercase ( A, unittest.TestCase ):
... | 360 | from __future__ import annotations
import unittest
from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import Co... | 35 | 0 |
import pytest
from datasets import inspect_metric, list_metrics, load_metric
@pytest.fixture
def _UpperCAmelCase ( a__):
'''simple docstring'''
monkeypatch.setattr("""datasets.utils.deprecation_utils._emitted_deprecation_warnings""" , set())
@pytest.fixture
def _UpperCAmelCase ( a... | 248 |
def _UpperCAmelCase ( ):
'''simple docstring'''
return [
a * b * (1_0_0_0 - a - b)
for a in range(1 , 9_9_9)
for b in range(a__ , 9_9_9)
if (a * a + b * b == (1_0_0_0 - a - b) ** 2)
][0]
if __name__ == "__main__":
print(F"""{solution() = }""")
| 248 | 1 |
from typing import Optional
from torch import nn
from .transformer_ad import TransformeraDModel, TransformeraDModelOutput
class UpperCamelCase_ ( nn.Module):
"""simple docstring"""
def __init__( self : int , UpperCAmelCase__ : int = 1_6 , UpperCAmelCase__ ... | 354 |
"""simple docstring"""
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
flip_channel_order,
get_resize_output_image_size,
rescale,
... | 195 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
if is_sentencepiece_available():
from ..... | 213 | """simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, log... | 213 | 1 |
import argparse
from pathlib import Path
from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration
def SCREAMING_SNAKE_CASE_ ( __A : int , __A : str , __A : str , __A : Path , __A : st... | 120 |
import argparse
import json
import logging
import os
import sys
from unittest.mock import patch
from transformers.testing_utils import TestCasePlus, get_gpu_count, slow
UpperCAmelCase_ : str = [
os.path.join(os.path.dirname(__file__), dirname)
for dirname in [
'text-classifi... | 120 | 1 |
def lowerCAmelCase_ ( __a ) -> int:
"""simple docstring"""
if not isinstance(__a , __a ):
raise TypeError("only integers accepted as input" )
else:
lowerCamelCase__: Tuple =str(abs(__a ) )
lowerCamelCase__: Union[str, Any] =[list(__a ) for c... | 10 |
from collections import defaultdict
from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst
def lowerCAmelCase_ ( ) -> Optional[int]:
"""simple docstring"""
lowerCamelCase__ , lowerCamelCase__: int =9, 14 # noqa: F841
lowerCamelCase__: Lis... | 10 | 1 |
"""simple docstring"""
def UpperCAmelCase__ (lowerCAmelCase_ , lowerCAmelCase_ = " " ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = []
__SCREAMING_SNAKE_CASE = 0
for index, char in enumerate(UpperCamelCase__ ):
if ... | 352 |
"""simple docstring"""
from __future__ import annotations
import requests
def UpperCAmelCase__ (lowerCAmelCase_ ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = f"""https://hacker-news.firebaseio.com/v0/item/{story_id}.json?print=pretty"""
return request... | 195 | 0 |
"""simple docstring"""
import argparse
import torch
from transformers import YosoConfig, YosoForMaskedLM
def __lowerCAmelCase (_UpperCamelCase ):
if "model" in orig_key:
__lowerCAmelCase : str = orig_key.replace('model.' , '' )
if "norm1" in orig_key:
__lowerCAmelCase... | 86 |
"""simple docstring"""
import math
import sys
def __lowerCAmelCase (_UpperCamelCase ):
if number != int(_UpperCamelCase ):
raise ValueError('the value of input must be a natural number' )
if number < 0:
raise ValueError('the value of input must not be a negative number' )
if number == 0:
ret... | 86 | 1 |
import argparse
import re
from pathlib import Path
import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor
from transformers import (
EfficientFormerConfig,
EfficientFormerForImageClassificationWithTeacher,
Efficien... | 35 | lowercase = {
"joule": 1.0,
"kilojoule": 1_0_0_0,
"megajoule": 1_0_0_0_0_0_0,
"gigajoule": 1_0_0_0_0_0_0_0_0_0,
"wattsecond": 1.0,
"watthour": 3_6_0_0,
"kilowatthour": 3_6_0_0_0_0_0,
"newtonmeter": 1.0,
"calorie_nutr": 4_1_8_6.8,
"kilocalorie_nutr": 4_1_8... | 35 | 1 |
import math
import sys
def lowercase_ ( _lowerCamelCase : int):
if number != int(_lowerCamelCase):
raise ValueError("the value of input must be a natural number")
if number < 0:
raise ValueError("the value of input must not be a negative number")
if number == 0:
... | 87 | UpperCamelCase = [0, 2, 4, 6, 8]
UpperCamelCase = [1, 3, 5, 7, 9]
def lowercase_ ( _lowerCamelCase : int , _lowerCamelCase : int , _lowerCamelCase : list[int] , _lowerCamelCase : int):
if remaining_length == 0:
if dig... | 87 | 1 |
'''simple docstring'''
UpperCamelCase = tuple[float, float, float]
UpperCamelCase = tuple[float, float, float]
def SCREAMING_SNAKE_CASE( __lowercase , __lowercase ) -> Vectorad:
A: Optional[Any] = end_pointa[0] - end_pointa[0]
A: ... | 334 |
'''simple docstring'''
import requests
UpperCamelCase = '''https://newsapi.org/v1/articles?source=bbc-news&sortBy=top&apiKey='''
def SCREAMING_SNAKE_CASE( __lowercase ) -> None:
# fetching a list of articles in json format
A: Tuple = requests.get(_NE... | 334 | 1 |
def UpperCAmelCase ( a_ , a_ ) -> int:
"""simple docstring"""
return x if y == 0 else greatest_common_divisor(a_ , x % y )
def UpperCAmelCase ( a_ , a_ ) -> int:
"""simple docstring"""
return (x * y) // greatest_common_divisor(a_ , a_ ... | 15 |
import argparse
from pathlib import Path
import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version
from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging
if version.parse(fairseq.__v... | 128 | 0 |
from collections import UserDict
from typing import Union
import numpy as np
import requests
from ..utils import (
add_end_docstrings,
logging,
)
from .audio_classification import ffmpeg_read
from .base import PIPELINE_INIT_ARGS, Pipeline
lowerCAmelCase_ : List[Any] = ... | 370 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
lowerCAmelCase_ : int = {
'''configuration_falcon''': ['''FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FalconConfig... | 170 | 0 |
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
ConditionalDetrConfig,
ConditionalDetrForObjectDetection,
ConditionalDetrForSegmentation,
... | 14 |
"""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 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, UNetaDConditionModel
from diffusers.utils impo... | 367 |
'''simple docstring'''
import argparse
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline
if __name__ == "__main__":
_A : List[Any] =argparse.ArgumentParser()
pars... | 129 | 0 |
import collections
import json
import os
import re
from typing import TYPE_CHECKING, List, Optional, Tuple
import numpy as np
from ...tokenization_utils_fast import PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
lowercase_ ... | 303 |
'''simple docstring'''
from pathlib import Path
import numpy as np
from PIL import Image
def lowercase__( __UpperCamelCase: np.ndarray ):
"""simple docstring"""
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Optional[Any] ... | 251 | 0 |
"""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
... | 366 |
"""simple docstring"""
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.generation import DisjunctiveConstraint
@require_torch
class _UpperCAmelCase ( unittest.TestCase )... | 68 | 0 |
"""simple docstring"""
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
A_ : Optional[int] = logging.get_logger(__n... | 165 |
"""simple docstring"""
import flax.linen as nn
import jax.numpy as jnp
from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD
class lowerCamelCase (nn.Module ):
lowerCamelCase__ : int
lowerCame... | 165 | 1 |
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel
from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import HeunDiscreteScheduler
fro... | 70 |
from typing import Optional
from torch import nn
from .transformer_ad import TransformeraDModel, TransformeraDModelOutput
class __magic_name__ ( nn.Module ):
"""simple docstring"""
def __init__( self :int , snake_case :int = 16 , snake_case :int = 88... | 70 | 1 |
'''simple docstring'''
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_torch_available():
import torch
if is_torch_tpu_avail... | 267 | """simple docstring"""
def a_ ( lowerCamelCase , lowerCamelCase ):
if a < 0 or b < 0:
raise ValueError('the value of both inputs must be positive' )
UpperCAmelCase__ = str(bin(lowerCamelCase ) )[2:] # remove the leading "0b"
UpperCAm... | 98 | 0 |
"""simple docstring"""
from math import factorial
A = {str(d): factorial(d) for d in range(10)}
def __A ( a_ :int) -> int:
return sum(DIGIT_FACTORIAL[d] for d in str(_snake_case))
def __A ( ) -> int:
__a : Any = 7... | 352 |
"""simple docstring"""
def __A ( a_ :int , a_ :int) -> str:
if a < 0 or b < 0:
raise ValueError('''the value of both inputs must be positive''')
__a : Union[str, Any] = str(bin(a_))[2:] # remove the leading "0b"
__a : Union[st... | 188 | 0 |
'''simple docstring'''
import unittest
from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch
class SCREAMING_SNAKE_CASE( unittest.TestCase ):
"""simple docstring"""
lowerCamelCase__ = JukeboxTokenizer... | 23 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase_ = {'''configuration_vit_msn''': ['''VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTMSNConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNo... | 244 | 0 |
"""simple docstring"""
import unittest
from parameterized import parameterized
from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_comm... | 351 |
"""simple docstring"""
def a__ ( SCREAMING_SNAKE_CASE : list[int] ):
'''simple docstring'''
lowerCAmelCase : str = len(SCREAMING_SNAKE_CASE )
for i in range(SCREAMING_SNAKE_CASE ):
for j in range(i + 1 , SCREAMING_SNAKE_CASE ):
if nu... | 133 | 0 |
import argparse
from collections import defaultdict
import yaml
_lowerCamelCase : str = "docs/source/en/_toctree.yml"
def a__ ( UpperCAmelCase : str ) -> List[str]:
UpperCAmelCase : Tuple = defaultdict(UpperCAmelCase )
for doc in model_doc:
counts[d... | 336 |
from queue import Queue
from typing import TYPE_CHECKING, Optional
if TYPE_CHECKING:
from ..models.auto import AutoTokenizer
class __UpperCAmelCase :
def __magic_name__ ( self : int, __A : Dict ):
raise NotImplementedError()
def ... | 336 | 1 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__lowerCAmelCase = logging.get_logger(__name__)
__lowerCAmelCase = '... | 288 |
import argparse
import os
import re
import tensorflow as tf
import torch
from transformers import BertConfig, BertModel
from transformers.utils import logging
logging.set_verbosity_info()
__lowerCAmelCase = logging.get_logger(__name__)
def snake_case_ ( snake_case ... | 288 | 1 |
import operator as op
SCREAMING_SNAKE_CASE__ = """scaler.pt"""
SCREAMING_SNAKE_CASE__ = """pytorch_model"""
SCREAMING_SNAKE_CASE__ = """random_states"""
SCREAMING_SNAKE_CASE__ = """optimizer"""
SCREAMING_SNAKE_CASE__ = """scheduler"""
SCREAMING_SNAKE_CASE__ = """... | 325 |
import os
import string
import sys
lowerCamelCase = 1 << 8
lowerCamelCase = {
'''tab''': ord('''\t'''),
'''newline''': ord('''\r'''),
'''esc''': 27,
'''up''': 65 + ARROW_KEY_FLAG,
'''down''': 66 + ARROW_KEY_FLAG,
'''right''': 67 + ARROW_KEY_FLAG,
'''left''': ... | 188 | 0 |
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
lowercase_ = """\
"""
lowercase_ = """
Perplexity (PPL) is one of the most common metrics for ev... | 269 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
lowercase_ = {
"""configuration_efficientformer""": [
"""EFFICIENTFORMER_PRETRAINED_CONFI... | 269 | 1 |
from typing import Dict, List, Optional, Union
import numpy as np
from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy
_A = logging.get_logger(__name__)
class UpperCAmelCa... | 62 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applic... | 62 | 1 |
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from PIL import Image
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from .... | 361 |
def lowerCamelCase ( a_ ) -> "list[int]":
if upper_limit < 0:
raise ValueError('Limit for the Catalan sequence must be ≥ 0' )
lowerCAmelCase_ = [0] * (upper_limit + 1)
# Base case: C(0) = C(1) = 1
lowerCAmelCase_ ... | 14 | 0 |
"""simple docstring"""
import unittest
from transformers import MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, AutoTokenizer, is_vision_available
from transformers.pipelines import pipeline
from transformers.pipelines.document_question_answering import apply_tesseract
from transformers.test... | 294 |
lowerCAmelCase__ = 0 # The first color of the flag.
lowerCAmelCase__ = 1 # The second color of the flag.
lowerCAmelCase__ = 2 # The third color of the flag.
lowerCAmelCase__ = (red, white, blue)
def __lowerCamelCase ( lowerCamelCase__ ):
"""simple docstring"""
... | 130 | 0 |
'''simple docstring'''
from manim import *
class __A ( UpperCamelCase__ ):
def _lowercase (self : Optional[Any] ):
UpperCAmelCase_ = Rectangle(height=0.5 , width=0.5 )
UpperCAmelCase_ = Rectangle(height=0.46 , width=0.46 ... | 106 | '''simple docstring'''
import argparse
import logging
import os
import sys
import numpy as np
import onnxruntime
import torch
from bart_onnx.generation_onnx import BARTBeamSearchGenerator
from bart_onnx.reduce_onnx_size import remove_dup_initializers
import transformers
from transformers import BartForConditiona... | 106 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A : List[str] = logging.get_logger(__name__)
A : int = {
"facebook/s2t-wav2vec2-large-en-de": (
"https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/c... | 57 |
"""simple docstring"""
import logging
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import arg_to_scheduler
from transformers import TrainingArguments
A : Dict = logging.getLogger(__name__)
@dataclass
class _UpperCamelCase ( lowerCAmelCase__ ):
... | 57 | 1 |
'''simple docstring'''
import itertools
import os
import re
__a: Any = re.compile(R"""([A-Z]+)([A-Z][a-z])""")
__a: Optional[Any] = re.compile(R"""([a-z\d])([A-Z])""")
__a: Optional[Any] = re.compile(R"""(?<!_)_(?!_)""")
__a: List[Any] = re.compile(R"""... | 361 | '''simple docstring'''
class UpperCAmelCase :
'''simple docstring'''
def __init__( self ) -> List[str]:
lowercase__ : Dict = {}
def _lowerCAmelCase( self ) -> None:
print(self.vertex )
for i in self.vertex:
print(__lowe... | 214 | 0 |
import argparse
import json
import logging
import os
import shutil
import sys
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.utils import write_basic_config
from transformers.testing_utils import TestCasePlus, get_gpu_count, run_command, slow, torch_device
from tr... | 338 | import numpy as np
def _UpperCamelCase ( snake_case__ ) -> np.ndarray:
return 1 / (1 + np.exp(-vector ))
def _UpperCamelCase ( snake_case__ ) -> np.ndarray:
return vector * sigmoid(snake_case__ )
if __name__ == "__main__":
... | 157 | 0 |
import warnings
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import Bat... | 14 |
from __future__ import annotations
from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass
@dataclass
class a_ :
'''simple docstring'''
__a: int
__a: int
class a_ :
... | 14 | 1 |
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
__snake_case = """"""
__snake_case = """"""
__snake_case = """"""
__snake_case = 1 # (0 is vertical, 1 is horizontal)
def _A ( ):
UpperCamelCase , Upper... | 259 |
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
from transformers.uti... | 259 | 1 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _UpperCamelCase ( lowerCAmelCase_ ):
_UpperCamelCase : List[Any] = ['''image_processor''', '''tokenizer''']
_UpperCamelCase : Optional[... | 328 |
from typing import Optional, Union
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin
class _UpperCamelCase ( lowerCAmelCase_ , lowerCAmelCase_ ):
@register_to_config
def __in... | 328 | 1 |
'''simple docstring'''
import re
import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey
from ..utils import logging
_lowerCamelCase : List[Any] = logging.get_logger(__name__)
def __lowerCamelCase ( ... | 28 |
"""simple docstring"""
from collections import defaultdict
class _UpperCAmelCase:
def __init__( self , __a , __a) -> Union[str, Any]:
'''simple docstring'''
_UpperCamelCase = total # total no of tasks (N)
# DP ... | 194 | 0 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
... | 368 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if ... | 280 | 0 |
"""simple docstring"""
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import torch
from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
@dataclass
class __A ( ... | 81 |
import argparse
import json
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import... | 38 | 0 |
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SegformerConfig,
SegformerForImageClassification,
SegformerForSemanticSe... | 354 |
'''simple docstring'''
from .configuration_bert_masked import MaskedBertConfig
from .modeling_bert_masked import (
MaskedBertForMultipleChoice,
MaskedBertForQuestionAnswering,
MaskedBertForSequenceClassification,
MaskedBertForTokenClassification,
MaskedBertModel,
)
from .modul... | 179 | 0 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_donut import DonutImageProcessor
A_ = logging.get_logger(__name__)
class _snake_case ( _a ):
def __init__( self : Optional[int] ,*SCREAMING_SNAKE_CASE__ : ... | 139 |
'''simple docstring'''
from __future__ import annotations
def A_ ( snake_case , snake_case , snake_case , ):
if (stress, tangential_force, area).count(0 ) != 1:
raise ValueError("You cannot supply more or less than 2 values" )
elif stress < 0... | 139 | 1 |
'''simple docstring'''
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import (
is_pt_flax_cross_test,
require_flax,
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import is_flax_available, is_torch_available... | 352 | '''simple docstring'''
import os
import time
import numpy as np
import onnxruntime as ort
snake_case__ : Optional[int] = '''1'''
snake_case__ : str = '''0'''
snake_case__ : List[str] = '''1'''
snake_case__ : List[str] = ort.SessionOptions... | 274 | 0 |
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