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"""
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 transformers.utils... | 373 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_ = {
'''tiiuae/falcon-40b''': '''https://huggingface.co/tiiuae/falcon-40b/resolve/main/config.j... | 373 | 1 |
from __future__ import annotations
import copy
import inspect
import unittest
import numpy as np
from transformers import is_tf_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from transformers.utils import cached_property
from... | 583 |
import os
import tempfile
import unittest
from transformers import NezhaConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_confi... | 583 | 1 |
def snake_case (UpperCAmelCase__ , UpperCAmelCase__ ) -> int:
return number | (1 << position)
def snake_case (UpperCAmelCase__ , UpperCAmelCase__ ) -> int:
return number & ~(1 << position)
def snake_case (UpperCAmelCase__ , ... | 57 |
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
execute_subprocess_async,
require_cpu,
require_huggingface_suite,
require_multi_gpu,
require_single_gpu,
)
from accelerate.... | 57 | 1 |
"""simple docstring"""
import hashlib
import unittest
from typing import Dict
import numpy as np
from transformers import (
MODEL_FOR_MASK_GENERATION_MAPPING,
TF_MODEL_FOR_MASK_GENERATION_MAPPING,
is_vision_available,
pipeline,
)
from transformers.pipelines import Mask... | 615 |
"""simple docstring"""
def _lowercase ( __snake_case = 3 ,__snake_case = 7 ,__snake_case = 1_000_000 ) -> int:
__lowerCAmelCase : Optional[Any] = 0
__lowerCAmelCase : List[str] = 1
for current_denominator in range(1 ,limit + 1 ... | 615 | 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 YolosConfig, YolosForObjectDetection, YolosImageProcessor
from transformers.utils import logging
logging.set_verb... | 209 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a = logging.get_logger(__name__)
a = {
'''facebook/s2t-small-librispeech-asr''': (
'''https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/config... | 7 | 0 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConfig,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaForCTC,
WavaVecaForPreTraining,
WavaVecaProcessor,
logging,
)
from transformers.mod... | 701 |
from __future__ import annotations
from fractions import Fraction
from math import gcd, sqrt
def _a ( UpperCAmelCase ) -> bool:
"""simple docstring"""
lowerCamelCase__ : int = int(number**0.5 )
return number == sq * sq
def _a ( UpperCAmelCase ... | 130 | 0 |
'''simple docstring'''
def _A ( A__ = 1000 ):
"""simple docstring"""
return sum(2 * a * ((a - 1) // 2) for a in range(3 , n + 1 ) )
if __name__ == "__main__":
print(solution())
| 41 |
import torch
from diffusers import DiffusionPipeline
class lowercase ( UpperCamelCase__ ):
def __init__( self , _a , _a ) -> List[str]:
super().__init__()
self.register_modules(unet=_a , scheduler=_a )
def __call__( self ... | 307 | 0 |
'''simple docstring'''
import argparse
import json
import os
import pickle
import shutil
import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset
from transformers import (
BertConfig,
BertForMaskedLM,
BertTokenizer,
DistilBertConfig,
DistilBertF... | 707 |
'''simple docstring'''
import argparse
import json
import os
import re
import shutil
import torch
from transformers import BioGptConfig, BioGptForCausalLM
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FIL... | 172 | 0 |
"""simple docstring"""
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis_float32 (there's also the fix_lavis branch)
# also note: to convert Vicuna checkpoints... | 83 |
"""simple docstring"""
def __snake_case ( __A : int , __A : int ) -> float:
'''simple docstring'''
return base * power(__A , (exponent - 1) ) if exponent else 1
if __name__ == "__main__":
print('Raise base to the power of exponent us... | 265 | 0 |
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: list ) -> list:
'''simple docstring'''
if len(SCREAMING_SNAKE_CASE_ ) < 2:
return collection
def circle_sort_util(SCREAMING_SNAKE_CASE_: list , SCREAMING_SNAKE_CASE_: int , SCREAMING_S... | 702 |
lowerCAmelCase__ = """ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"""
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: bytes ) -> bytes:
'''simple docstring'''
if not isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
A__... | 626 | 0 |
import numpy as np
def lowerCamelCase__ (_UpperCAmelCase):
return 1 / (1 + np.exp(-vector))
def lowerCamelCase__ (_UpperCAmelCase):
return vector * sigmoid(_UpperCAmelCase)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 73 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
_A = {
'''configuration_falcon''': ['''FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FalconConfig'''],
}
try:
if not is_torch_available():
raise Op... | 431 | 0 |
'''simple docstring'''
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
... | 58 | '''simple docstring'''
import os
from argparse import ArgumentParser, Namespace
from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand
if not is... | 58 | 1 |
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class lowerCAmelCase_ ( snake_case__ ):
Up... | 668 |
import json
import os
from typing import Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_UpperCAmelCase : str = logging.get_logger(__name__)
_UpperCAmelCase : Dict = {"vocab_file": "vocab.json"}
_UpperCAmelCase : Optiona... | 668 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A = logging.get_logger(__name__)
_A = {
'unc-nlp/lxmert-base-uncased': 'https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/config.json',
}
class _lowercase ... | 133 |
"""simple docstring"""
def UpperCAmelCase ( a_ ):
'''simple docstring'''
try:
lowerCamelCase : List[str] = float(a_ )
except ValueError:
raise ValueError('Please enter a valid number' )
lowerCamelCase : Dict = decimal - int(a_ )
if ... | 133 | 1 |
# Author: OMKAR PATHAK, Nwachukwu Chidiebere
# Use a Python dictionary to construct the graph.
from __future__ import annotations
from pprint import pformat
from typing import Generic, TypeVar
__a : Any = TypeVar('''T''')
class UpperCAmelCase( Generic[T] ):
... | 397 |
from __future__ import annotations
import bisect
def SCREAMING_SNAKE_CASE ( snake_case_ : list[int] , snake_case_ : int , snake_case_ : int = 0 , snake_case_ : int = -1 ):
if hi < 0:
snake_case__ : Any = len(snake_case_ )
while lo < hi:
snake_case_... | 297 | 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 (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channe... | 687 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
__snake_case : Union[str, Any] = {
'configuration_falcon': ['FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FalconConfig'],
}
try:
if not is_t... | 687 | 1 |
def __snake_case ( _lowerCAmelCase : int ) -> bool:
return str(_lowerCAmelCase ) == str(_lowerCAmelCase )[::-1]
def __snake_case ( _lowerCAmelCase : int ) -> int:
return int(_lowerCAmelCase ) + int(str(_lowerCAmelCase )[::-1] )
def... | 454 |
def __snake_case ( _lowerCAmelCase : list , _lowerCAmelCase : list , _lowerCAmelCase : int ) -> int:
if len(_lowerCAmelCase ) != len(_lowerCAmelCase ):
raise ValueError("The length of profit and weight must be same." )
if max_weight <= 0:... | 454 | 1 |
import os
from argparse import ArgumentParser
from typing import List
import torch.utils.data
from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node
UpperCamelCase__ = 4
UpperCamelCase__ = 3
class _UpperCamelCase ( ... | 700 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase__ : List[Any] = {"""configuration_sew""": ["""SEW_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SEWConfig"""]}
try:
if not is_torch_available():
raise ... | 486 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase__ : Optional[int] = logging.get_logger(__name__)
UpperCAmelCase__ : Tuple = {
"facebook/dpr-ctx_encoder-single-nq-base": (
"https://huggingface.co/facebook/dp... | 48 |
"""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, loggin... | 555 | 0 |
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 transformers.utils import logging
logging.... | 721 |
from __future__ import annotations
import unittest
from transformers import LEDConfig, 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
from ...test_pipeline_mixin im... | 148 | 0 |
'''simple docstring'''
def UpperCAmelCase__ ( UpperCAmelCase_ : list , UpperCAmelCase_ : list ) -> float:
_validate_point(UpperCAmelCase_ )
_validate_point(UpperCAmelCase_ )
if len(UpperCAmelCase_ ) != len(UpperCAmelCase_ ):
ra... | 13 |
'''simple docstring'''
import re
def _snake_case ( _SCREAMING_SNAKE_CASE : str ) -> bool:
"""simple docstring"""
lowerCAmelCase = re.compile(
R"""^(?:0|94|\+94|0{2}94)""" R"""7(0|1|2|4|5|6|7|8)""" R"""(-| |)""" R"""\d{7}$""" )
retur... | 433 | 0 |
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
A_ = logging.ge... | 360 |
def __UpperCamelCase ( a, a, a=False) ->Dict:
if isinstance(a, a) and isinstance(a, a):
lowerCamelCase__ = len(set_a.intersection(a))
if alternative_union:
lowerCamelCase__ = len(a) + len(a)
else:
... | 360 | 1 |
'''simple docstring'''
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.ima... | 161 |
'''simple docstring'''
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, Levi... | 161 | 1 |
import io
import json
import fsspec
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.json import JsonDatasetReader, JsonDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def _a ( __UpperCamelCase , ... | 702 |
import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List
from ..utils import logging
__lowerCamelCase = logging.get_logger(__name__)
def _a ( __UpperCamelCase=None , __UpperCamelCase=None ):
return field(defa... | 478 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__UpperCamelCase = {
"configuration_rag": ["RagConfig"],
"retrieval_rag": ["RagRetriever"],
"tokenization_... | 26 |
'''simple docstring'''
import inspect
import unittest
import torch
import torch.nn as nn
from accelerate.hooks import (
AlignDevicesHook,
ModelHook,
SequentialHook,
add_hook_to_module,
attach_align_device_hook,
remove_hook_from_module,
remove_hook_from... | 26 | 1 |
import unittest
from diffusers.models.unet_ad_blocks import * # noqa F403
from diffusers.utils import torch_device
from .test_unet_blocks_common import UNetBlockTesterMixin
class _a ( lowerCAmelCase__ , unittest.TestCase ):
'''simple docstring'''
lowerCamelCase_ : Opti... | 718 | def lowerCamelCase_ ( _lowercase = 2_000_000 ) -> int:
__A : str = [0 for i in range(n + 1 )]
__A : int = 1
__A : Dict = 1
for i in range(2 , int(n**0.5 ) + 1 ):
if primality_list[i] == 0:
... | 387 | 0 |
"""simple docstring"""
import argparse
import collections
import json
from pathlib import Path
import requests
import torch
import yaml
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileViTImageProcessor,
MobileViTVaConfig,
MobileViTVaForImageClassific... | 626 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transfor... | 626 | 1 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionSAGPipeline,
UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers... | 707 |
from collections.abc import Callable
def _UpperCAmelCase ( a : Callable[[float], float] , a : float , a : float ):
snake_case__ = a
snake_case__ = b
if function(a ) == 0: # one of the a or b is a root for the function
return a
... | 99 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_lowerCAmelCase : List[Any] = {
"configuration_remb... | 289 |
"""simple docstring"""
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis
from lavis.models import load_model_and_preprocess
from PIL import Image
from transform... | 289 | 1 |
import gc
import inspect
import unittest
import torch
from parameterized import parameterized
from diffusers import PriorTransformer
from diffusers.utils import floats_tensor, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_model... | 712 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowercase : Union[str, Any] =logging.get_logger(__name__)
__lowercase : List[Any] ={
"""tanreinama/GPTSAN-2.8B-spout_is_uniform""": (
"""https://huggingface.co/tanreinama/GPT... | 550 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
if is_sentencepiece_available():
from ..ta.tokenizatio... | 257 |
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, PLBartTokenizer, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
)
from ...test_tokeniza... | 257 | 1 |
import warnings
from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor
A_ : Any = logging.get_logger(__name__)
class _a (__magic_name__ ):
'''simple docstring'''
def __init__( self , *A__ , **A__ ):
warnings.wa... | 64 |
def UpperCamelCase (lowercase_: int , lowercase_: int ) -> int:
while second != 0:
A__ : int = first & second
first ^= second
A__ : int = c << 1
return first
if __name__ == "__main__":
import doctest
doctest.testmod()
A_ : Optional[Any] ... | 64 | 1 |
from __future__ import annotations
def snake_case (UpperCAmelCase__ , UpperCAmelCase__ ) -> list[str]:
if nth_term == "":
return [""]
UpperCamelCase_: List[str] = int(UpperCAmelCase__ )
UpperCamelCase_: Any = int(UpperCAmelCase_... | 57 |
'''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 GenerationTesterMi... | 502 | 0 |
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_available(... | 719 |
def lowercase_ ( __snake_case : list ) -> list:
'''simple docstring'''
if any(not isinstance(__snake_case , __snake_case ) or x < 0 for x in sequence ):
raise TypeError("Sequence must be list of non-negative integers" )
for _ ... | 57 | 0 |
"""simple docstring"""
import html
from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from ...utils import is_bsa_available, logging, requires_backends
if is_bsa_available():
import bsa
from bsa import BeautifulSoup
_lowerCAmelCase = logging.get_... | 259 |
"""simple docstring"""
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_com... | 259 | 1 |
import sys
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before tokenize... | 704 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCAmelCase = {
"""configuration_nllb_moe""": [
"""NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""NllbMoeConfig""",
]
}
try:
if not is_torch_available(... | 481 | 0 |
def _SCREAMING_SNAKE_CASE ( __snake_case , __snake_case ) -> float:
if mass < 0:
raise ValueError("""The mass of a body cannot be negative""" )
return 0.5 * mass * abs(__snake_case ) * abs(__snake_case )
if __name__ == "__main__":
import doctest
docte... | 108 |
from itertools import permutations
def lowerCAmelCase__ ( _a : tuple ):
if num[3] % 2 != 0:
return False
if (num[2] + num[3] + num[4]) % 3 != 0:
return False
if num[5] % 5 != 0:
return False
snake_case_ : List[str] = [7, 11, 13, 17]
for i, te... | 568 | 0 |
'''simple docstring'''
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
lowerCAmelCase : List[str] = [
os.path.join(os.path.dirname(__file__), dirname)
fo... | 432 |
'''simple docstring'''
lowerCAmelCase : Optional[Any] = '\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 ... | 432 | 1 |
from .configuration_bert_masked import MaskedBertConfig
from .modeling_bert_masked import (
MaskedBertForMultipleChoice,
MaskedBertForQuestionAnswering,
MaskedBertForSequenceClassification,
MaskedBertForTokenClassification,
MaskedBertModel,
)
from .modules import *
| 479 |
'''simple docstring'''
# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
# python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files n... | 597 | 0 |
from typing import Any, Callable, Dict, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusionPip... | 711 |
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
def __lowerCamelCase ( _lowerCAmelCase ... | 129 | 0 |
import multiprocessing
import time
from arguments import PretokenizationArguments
from datasets import load_dataset
from transformers import AutoTokenizer, HfArgumentParser
def _SCREAMING_SNAKE_CASE ( __snake_case ) -> str:
_UpperCAmelCase = {}
_UpperCAmelCase ... | 108 |
from __future__ import annotations
import copy
import inspect
import unittest
import numpy as np
from transformers import is_tf_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from transformers.utils import cached_property
from... | 514 | 0 |
'''simple docstring'''
from collections import namedtuple
import requests
from lxml import html # type: ignore
lowerCamelCase_ : Any = namedtuple('''covid_data''', '''cases deaths recovered''')
def __magic_name__( _A = "https://www.worldometers.info/coronavirus/" ):
'''simple ... | 265 |
'''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 _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE ):
'''simple docs... | 265 | 1 |
import functools
import logging
import os
import sys
import threading
from logging import (
CRITICAL, # NOQA
DEBUG, # NOQA
ERROR, # NOQA
FATAL, # NOQA
INFO, # NOQA
NOTSET, # NOQA
WARN, # NOQA
WARNING, # NOQA
)
from typing import Optional
import huggingface... | 684 | def A ( _lowercase , _lowercase ):
SCREAMING_SNAKE_CASE : Dict = word.split()
def justify(_lowercase , _lowercase , _lowercase ) -> str:
SCREAMING_SNAKE_CASE : Union[str, Any] = max_width - width
... | 248 | 0 |
import math
from datetime import datetime, timedelta
def UpperCamelCase ( _A : Tuple )-> str:
"""simple docstring"""
A__ = year % 19
A__ = year % 4
A__ = year % 7
A__ = math.floor(year / 100 )
A__ = math.floo... | 703 |
def UpperCamelCase ( _A : list[list[int]] , _A : int , _A : int , _A : list[int] )-> bool:
"""simple docstring"""
if graph[path[curr_ind - 1]][next_ver] == 0:
return False
# 2. Validate that next vertex is n... | 232 | 0 |
_lowerCAmelCase : dict[str, float] = {
"km/h": 1.0,
"m/s": 3.6,
"mph": 1.609344,
"knot": 1.852,
}
_lowerCAmelCase : dict[str, float] = {
"km/h": 1.0,
"m/s": 0.277777778,
"mph": 0.621371192,
"knot": 0.539956803,
}
def ... | 242 |
import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict
import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info
from transformers import (
AdamW,
AutoConfig,
AutoModel,
AutoModelForPreTraining,
AutoModelForQuest... | 242 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__lowerCamelCase = {
'configuration_conditional_detr': [
'CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP',
'ConditionalD... | 702 |
'''simple docstring'''
from __future__ import annotations
import sys
from collections import deque
from typing import Generic, TypeVar
__lowerCamelCase : Optional[int] = TypeVar('T')
class UpperCAmelCase ( Generic[T]):
"""simple docstring"""
lowerCAmelCase_ = 42 ... | 271 | 0 |
from typing import Optional, Union
import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention
from ...modeling_utils import PreTr... | 89 |
'''simple docstring'''
from typing import Union
import fire
import torch
from tqdm import tqdm
def SCREAMING_SNAKE_CASE_ ( __A : str , __A : str = "cpu" , __A : Union[str, None] = None ) -> None:
_SCREAMING_SNAKE_CASE = torch.load(__A , map_locati... | 418 | 0 |
import math
def __lowerCamelCase ( A__ : int ) -> bool:
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All p... | 171 |
import argparse
import pytorch_lightning as pl
import torch
from torch import nn
from transformers import LongformerForQuestionAnswering, LongformerModel
class SCREAMING_SNAKE_CASE_ (pl.LightningModule ):
'''simple docstring'''
def __init__( self : Any , __a ... | 171 | 1 |
def SCREAMING_SNAKE_CASE__ ( __lowerCAmelCase ):
# if the collection is empty, returns empty
if collection == []:
return []
# get some information about the collection
snake_case__ = len(__lowerCAmelCase )
snake_case__ = max(__lowerCAmelCase )
snake_c... | 276 |
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 BartForConditionalGeneration, BartTokenizer
... | 276 | 1 |
import warnings
from contextlib import contextmanager
from ....processing_utils import ProcessorMixin
class __UpperCamelCase ( _lowercase ):
"""simple docstring"""
_lowercase : Optional[Any] = '''MCTCTFeatureExtractor'''
_lowercase : str = '''AutoTokenizer... | 716 |
def __a ( __UpperCAmelCase , __UpperCAmelCase ):
# Check if the input is valid
if not len(__UpperCAmelCase ) == len(__UpperCAmelCase ) == 3:
raise ValueError('''Please enter a valid equation.''' )
if equationa[0] == equationa[1] == equationa[0] == equationa[1] == 0:
raise... | 148 | 0 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
SCREAMING_SNAKE_CASE = {'tokenization_wav2vec2_phoneme': ['Wav2Vec2PhonemeCTCTokenizer']}
if TYPE_CHECKING:
from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
import sys
SCREAMING_S... | 99 |
'''simple docstring'''
import json
import os
from typing import Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_UpperCamelCase = logging.get_logger(__name__)
_UpperCamelCase = {'vocab_file': 'vocab.json'}
_Uppe... | 459 | 0 |
"""simple docstring"""
import tensorflow as tf
from ...tf_utils import shape_list
class lowercase_ ( tf.keras.layers.Layer ):
'''simple docstring'''
def __init__( self : Optional[int] , _UpperCAmelCase : Dict , _UpperCAmelCase : Dict , _UpperCAmelCase : Li... | 714 |
"""simple docstring"""
import multiprocessing
import time
from arguments import PretokenizationArguments
from datasets import load_dataset
from transformers import AutoTokenizer, HfArgumentParser
def _snake_case ( _snake_case : List[Any] ) -> Any:
'''simple do... | 505 | 0 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required... | 166 | from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
A__ = {"""configuration_opt""": ["""OPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """OPTConfig"""]... | 166 | 1 |
from collections import deque
class UpperCAmelCase__ :
def __init__( self ,A__ ,A__ ,A__ ):
_A : int = process_name # process name
_A : str = arrival_time # arrival time of the process
# completion time... | 713 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_UpperCamelCase : Dict =logging.get_logger(__name__)
_UpperCamelCase : Optional[Any] ={
'facebook/xmod-bas... | 332 | 0 |
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
class UpperCAmelCase__ ( unittest.TestCase , A_ ):
'''simple docstring'''
def lowerCAmelCase_ ( self : Optional[int] ):
"""simple docstring"""
... | 322 |
import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
UpperCamelCase__ = logging.get_logger(__name__)
class UpperCAmelCase__ ( A_ ):
'''simple docstring'''
def __init__( self : int , *UpperCamelCase : ... | 322 | 1 |
from ..utils import is_flax_available, is_torch_available
if is_torch_available():
from .autoencoder_kl import AutoencoderKL
from .controlnet import ControlNetModel
from .dual_transformer_ad import DualTransformeraDModel
from .modeling_utils import ModelMixin
from .prior_transf... | 718 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class A__ ( __UpperCAmelCase ):
"""simple docstring"""
__A : Tuple = ['''image_processor''', '''tokenizer''']
__A : Any = ... | 392 | 0 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.... | 588 |
'''simple docstring'''
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from transformers import (
VisualBertConfig,
VisualBertForMultipleChoice,
VisualBertForPreTraining,
VisualBertForQuestionAnswering,
VisualBertForVisualReasoning,
)
from transforme... | 588 | 1 |
from __future__ import annotations
def __lowerCAmelCase( __UpperCAmelCase ,__UpperCAmelCase ):
"""simple docstring"""
if partitions <= 0:
raise ValueError('partitions must be a positive number!' )
if partitions > number_of_bytes:
raise ValueError('partitions can not > number_of_bytes!'... | 709 | """simple docstring"""
from __future__ import annotations
import collections
import pprint
from pathlib import Path
def __lowerCAmelCase( __UpperCAmelCase ):
"""simple docstring"""
return "".join(sorted(__UpperCAmelCase ) )
def __lowerCAmelCase( __UpperCAmelCase ):
""... | 283 | 0 |
"""simple docstring"""
import unittest
from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property
from ...test_tokenization_co... | 7 |
'''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, l... | 374 | 0 |
import contextlib
import os
import sqlitea
import pytest
from datasets import Dataset, Features, Value
from datasets.io.sql import SqlDatasetReader, SqlDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases, require_sqlalchemy
def a__ (... | 332 |
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 ver... | 332 | 1 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_deformable_detr import DeformableDetrImageProcessor
UpperCAmelCase = logging.get_logger(__name__)
class lowercase__ ( A_ ):
def __init__( self , *SCREAMING_SN... | 88 |
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel
from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class... | 412 | 0 |
"""simple docstring"""
import timeit
import numpy as np
import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD
def A_ ( __UpperCamelCase : int ):
def wrapper(*__UpperCamelCase : List[Any] , **__Upper... | 704 |
"""simple docstring"""
import requests
from bsa import BeautifulSoup
def A_ ( __UpperCamelCase : str , __UpperCamelCase : dict ):
lowercase = BeautifulSoup(requests.get(__UpperCamelCase , params=__UpperCamelCase ).content , '''html.parser''' ... | 396 | 0 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_convbert import ConvBertTokenizer
_UpperCamelCase : Any = logging.g... | 541 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
_lowerCamelCase = {'tokenization_wav2vec2_phoneme': ['Wav2Vec2PhonemeCTCTokenizer']}
if TYPE_CHECKING:
from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
import sys
_lowerCamelCase ... | 144 | 0 |
from __future__ import annotations
def __SCREAMING_SNAKE_CASE ( __UpperCamelCase : list[int] , __UpperCamelCase : int ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = 0
SCREAMING_SNAKE_CASE__ = len(Upper... | 710 | import os
__lowerCamelCase : Union[str, Any] = {'''I''': 1, '''V''': 5, '''X''': 10, '''L''': 50, '''C''': 100, '''D''': 500, '''M''': 1000}
def __SCREAMING_SNAKE_CASE ( __UpperCamelCase : str ) -> int:
"""simple docstring"""
SCREAMI... | 379 | 0 |
import inspect
import unittest
from transformers import SegformerConfig, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common ... | 219 |
import tempfile
import unittest
import numpy as np
from diffusers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionPipeline,
PNDMScheduler,
)
from diffusers.utils.testing_utils import is_... | 219 | 1 |
'''simple docstring'''
import argparse
import torch
# Step 1. clone https://github.com/microsoft/unilm
# Step 2. git checkout to https://github.com/microsoft/unilm/commit/b94ec76c36f02fb2b0bf0dcb0b8554a2185173cd
# Step 3. cd unilm
# Step 4. ln -s $(realpath wavlm/modules.py) ./ # create simlink
# import clas... | 123 |
'''simple docstring'''
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class UpperCamelCase__ ( a ):
'''simple docstring'''
@staticmethod
@abstractmethod
def snake_case ( SCREAMING_SNAKE_CASE ) -> Optional[Any]:
... | 123 | 1 |
"""simple docstring"""
import argparse
from pathlib import Path
from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration
def lowercase ( lowerCAmelCase__ ,lowerCAmelCase__ ,lowerCAmelCase__ ,lowerCAmelCase__ ,lowerCAmelCase__ = None ,lowerCAmelCase__... | 29 |
"""simple docstring"""
import argparse
import torch
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt
if __name__ == "__main__":
SCREAMING_SNAKE_CASE : Tuple = argparse.ArgumentParser()
parser.add_argument(
'''--check... | 156 | 0 |
"""simple docstring"""
import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple
import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from ut... | 91 |
"""simple docstring"""
# HF Trainer benchmarking tool
#
# This tool can be used to run and compare multiple dimensions of the HF Trainers args.
#
# It then prints a report once in github format with all the information that needs to be shared
# with others and second time in a console-friendly format, so it's easi... | 91 | 1 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import DistilBertConfig, 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_tens... | 109 |
"""simple docstring"""
from string import ascii_uppercase
lowerCAmelCase__ = {str(ord(c) - 55): c for c in ascii_uppercase}
def a__ ( SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
if isinstance(SCREAMING... | 645 | 0 |
"""simple docstring"""
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> int:
"""simple docstring"""
__UpperCAmelCase , __UpperCAmelCase : str = len(UpperCamelCase ), len(grid[0] )
if (
... | 487 |
"""simple docstring"""
# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import flax
import jax
import jax.numpy as jnp
from ..configuration_utils import ConfigMixin, register_to_config
from .sche... | 487 | 1 |
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,
AutoTokenizer,
DataCo... | 10 | import sys
from collections import defaultdict
class lowerCAmelCase_ :
def __init__( self : Optional[int] ):
_UpperCamelCase = []
def UpperCamelCase_ ( self : Any , _A : str ):
return self.node_position[verte... | 10 | 1 |
import inspect
import math
import tempfile
import unittest
import numpy as np
from transformers import ViTMAEConfig
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_configura... | 46 |
import argparse
import intel_extension_for_pytorch as ipex
import torch
from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline
A = argparse.ArgumentParser('Stable Diffusion script with intel optimization', add_help=False)
parser.add_argument('--dpm', action='store_true', help='En... | 46 | 1 |
"""simple docstring"""
def lowerCamelCase_ ( UpperCAmelCase_ , UpperCAmelCase_ = " " ) ->list:
"""simple docstring"""
__UpperCAmelCase : Tuple = []
__UpperCAmelCase : Union[str, Any] = 0
for index, char in enumerate(Uppe... | 522 |
"""simple docstring"""
# 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.... | 522 | 1 |
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import Callable, Dict, List, Tuple
import timm
import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams, RegNetYaagf, RegNetYaagf, RegNetYa... | 711 |
def __lowerCAmelCase ( UpperCamelCase ) -> bool:
if not isinstance(UpperCamelCase , UpperCamelCase ):
raise ValueError('''Input series is not valid, valid series - [2, 4, 6]''' )
if len(UpperCamelCase ) == 0:
raise ValueError('''Input list must be a non empty lis... | 470 | 0 |
'''simple docstring'''
import unittest
from dataclasses import dataclass
import pytest
from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict
@dataclass
class UpperCAmelCase_ ( __... | 94 |
'''simple docstring'''
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... | 368 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE = {
'configuration_lilt': ['LILT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LiltConfig'],
}
try:
if not is_torch_available():
... | 714 | """simple docstring"""
import functools
def __lowerCAmelCase( __UpperCAmelCase ,__UpperCAmelCase ):
"""simple docstring"""
if not isinstance(__UpperCAmelCase ,__UpperCAmelCase ) or not all(isinstance(__UpperCAmelCase ,__UpperCAmelCase ) for day in days ):
raise ValueErr... | 283 | 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,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
Sta... | 696 |
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=A__ )
class _lowercase ( A__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : str = field(default=''... | 696 | 1 |
'''simple docstring'''
def _a ( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ):
return not any(
neighbour == 1 and colored_vertices[i] == color
for i, neighbour in enumerate(lowerCamelCase_ ) )
def _a ( lowerCamelCase_ , lowerCamelCase_ , lo... | 718 |
'''simple docstring'''
from typing import Tuple, Union
from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone ... | 136 | 0 |
"""simple docstring"""
def _UpperCamelCase ( UpperCamelCase ) -> bool:
"""simple docstring"""
if p < 2:
raise ValueError("p should not be less than 2!" )
elif p == 2:
return True
__UpperCAmelCase : List[Any] = 4
__UpperCAme... | 77 |
"""simple docstring"""
import os
import unittest
from tempfile import TemporaryDirectory
import torch
import torch.nn as nn
from accelerate.utils import (
OffloadedWeightsLoader,
extract_submodules_state_dict,
load_offloaded_weight,
offload_state_dict,
offload_weight,
)
class a__ ... | 77 | 1 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ = {"""configuration_mmbt""": ["""MMBTConfig"""]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_... | 622 |
import torch
from diffusers import StableDiffusionPipeline
a_ = """path-to-your-trained-model"""
a_ = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to("""cuda""")
a_ = """A photo of sks dog in a bucket"""
a_ = pipe(prompt, num_inference_steps=50, guidance_s... | 622 | 1 |
'''simple docstring'''
import os
import unittest
from transformers import FunnelTokenizer, FunnelTokenizerFast
from transformers.models.funnel.tokenization_funnel import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMix... | 536 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Iterator
from typing import Generic, TypeVar
__lowerCAmelCase = TypeVar("T")
class __SCREAMING_SNAKE_CASE (Generic[T] ):
"""simple docstring"""
def __init__( self , UpperCamel... | 536 | 1 |
'''simple docstring'''
import argparse
from copy import deepcopy
import numpy as np
from datasets import ClassLabel, DatasetDict, load_dataset
from evaluate import load
from transformers import (
AutoModelForSequenceClassification,
AutoTokenizer,
DataCollatorWithPadding,
Trainer,
TrainerCall... | 434 |
'''simple docstring'''
from __future__ import annotations
from math import gcd
def lowercase__ ( __lowercase : int , __lowercase : int = 2 , __lowercase : int = 1 , __lowercase : int = 3 , ) -> int | None:
"""simple... | 434 | 1 |
"""simple docstring"""
from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors
def _lowerCamelCase ( UpperCAmelCase_ : int ) -> int:
"""simple docstring"""
A__ = prime_factors(UpperCAmelCase... | 104 |
def lowerCamelCase__ ( snake_case_ : str , snake_case_ : list[str] ) -> str:
__snake_case = ''''''
for word_or_phrase in separated:
if not isinstance(snake_case_ , snake_case_ ):
raise Exception('''join() accepts only strings to be joine... | 592 | 0 |
import unittest
from diffusers import FlaxAutoencoderKL
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax
from .test_modeling_common_flax import FlaxModelTesterMixin
if is_flax_available():
import jax
@require_flax
class snake_case_ ( _... | 701 |
import json
import os
import unittest
from transformers.models.roc_bert.tokenization_roc_bert import (
VOCAB_FILES_NAMES,
RoCBertBasicTokenizer,
RoCBertTokenizer,
RoCBertWordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.testing_utils import require_... | 240 | 0 |
"""simple docstring"""
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis
from lavis.models import load_model_and_preprocess
from PIL import Image
... | 49 |
"""simple docstring"""
__UpperCAmelCase : List[str] = {str(digit): digit**5 for digit in range(10)}
def A ( _A ):
"""simple docstring"""
return sum(DIGITS_FIFTH_POWER[digit] for digit in str(_A ) )
def A ( ):
"""simple docstring"""
return... | 584 | 0 |
'''simple docstring'''
import argparse
import json
import subprocess
def lowercase__ ( __lowercase : int , __lowercase : Any ) -> List[Any]:
"""simple docstring"""
__UpperCamelCase = []
__UpperCamelCase = (
F'''curl -H... | 434 |
'''simple docstring'''
import importlib.metadata
import warnings
from copy import deepcopy
from packaging import version
from ..utils import logging
from .import_utils import is_accelerate_available, is_bitsandbytes_available
if is_bitsandbytes_available():
import bitsandbytes as bnb
import torch
... | 434 | 1 |
"""simple docstring"""
import argparse
import json
import pickle
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcess... | 560 |
'''simple docstring'''
from __future__ import annotations
def __lowerCamelCase ( __lowerCAmelCase : Any , __lowerCAmelCase : str , __lowerCAmelCase : Any , __lowerCAmelCase : int ) -> Tuple: # noqa: E741
... | 369 | 0 |
"""simple docstring"""
# Copyright 2022 The HuggingFace Team and The OpenBMB 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/lic... | 715 |
"""simple docstring"""
import tempfile
import unittest
from make_student import create_student_by_copying_alternating_layers
from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch
lowerCAmelCase : Optional[Any] ... | 533 | 0 |
'''simple docstring'''
from typing import Dict, Optional
import numpy as np
import datasets
A : Dict = '''
IoU is the area of overlap between the predicted segmentation and the ground truth divided by the area of union
between the predicted segmentation and the ground truth. For binar... | 349 |
"""simple docstring"""
import json
import os
import unittest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@requ... | 46 | 0 |
'''simple docstring'''
def UpperCAmelCase_ ( lowerCamelCase_ ):
"""simple docstring"""
lowerCAmelCase__ : Optional[int] = len(lowerCamelCase_ )
for i in range(length - 1 ):
lowerCAmelCase__ : Optional[Any] = i
for k in range(i + 1 , lowerCamelCase_ ):
if col... | 568 |
'''simple docstring'''
import os
import re
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
snake_case = logging.get_logger(__name__)
snake_case = {
"""vocab_file""": """vocab.t... | 568 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__snake_case :str =logging.get_logger(__name__)
__snake_case :Tuple ={
'facebook/s2t-wav2vec2-large-en-de': (
'https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/config.json'
... | 106 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import is_flaky, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
... | 3 | 0 |
"""simple docstring"""
import inspect
import os
import torch
from transformers import AutoModel
from transformers.testing_utils import mockenv_context
from transformers.trainer_utils import set_seed
import accelerate
from accelerate.accelerator import Accelerator
from accelerate.state import Accele... | 706 | """simple docstring"""
import os
import unittest
from transformers.models.cpmant.tokenization_cpmant import VOCAB_FILES_NAMES, CpmAntTokenizer
from transformers.testing_utils import require_jieba, tooslow
from ...test_tokenization_common import TokenizerTesterMixin
@require_jieba
class UpperCamel... | 536 | 0 |
'''simple docstring'''
import os
import pytest
from transformers.dynamic_module_utils import get_imports
_lowerCAmelCase = '''
import os
'''
_lowerCAmelCase = '''
def foo():
import os
return False
'''
_lowerCAmelCase = '''
def foo():
def bar():
if True:
... | 565 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_lowerCAmelCase = {'''configuration_opt''': ['''OPT_PRETRAINED_CONFIG_... | 565 | 1 |
from ...processing_utils import ProcessorMixin
class a__ ( __SCREAMING_SNAKE_CASE ):
_A = ["image_processor", "feature_extractor"]
_A = "TvltImageProcessor"
_A = "TvltFeatureExtractor"
def __init__( self : Union[str, Any] , ... | 702 | import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelera... | 584 | 0 |
'''simple docstring'''
import argparse
import json
import os
from collections import OrderedDict
import numpy as np
import tensorflow as tf
import torch
def __UpperCAmelCase ( __magic_name__ )-> Union[str, Any]:
"""simple docstring"""
snake_case... | 653 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCamelCase : Optional[int] = {
"configuration_jukebox": [
"JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP",
"JukeboxConfig",
"JukeboxPriorConf... | 468 | 0 |
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.text import TextDatasetReader
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def lowerCAmelCase_ ( __lowerCamelCase , __lowerCamelCase ):
... | 717 |
import collections
import inspect
import unittest
from transformers import FocalNetConfig
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_backbone_common import BackboneTest... | 203 | 0 |
from __future__ import annotations
from fractions import Fraction
def a_ ( __magic_name__ , __magic_name__ ) -> Any:
"""simple docstring"""
return (
num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num / den
)
def ... | 598 |
import argparse
from pathlib import Path
import torch
from packaging import version
from torch.onnx import export
from diffusers import AutoencoderKL
UpperCamelCase__ = version.parse(version.parse(torch.__version__).base_version) < version.parse('''1.11''')
def lowerCamelCas... | 268 | 0 |
"""simple docstring"""
import io
import math
from typing import Dict, Optional, Union
import numpy as np
from huggingface_hub import hf_hub_download
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import convert_to_rgb, normalize, to_channel_dimension_for... | 712 |
"""simple docstring"""
import torch
from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class __a ( _lowerCAmelCase ):
UpperCamelCase_ : Any = (EulerDiscreteScheduler,)
UpperCamelCase... | 556 | 0 |
"""simple docstring"""
import unittest
from transformers import (
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
Pipeline,
ZeroShotClassificationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, re... | 277 |
'''simple docstring'''
# 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 mod... | 466 | 0 |
'''simple docstring'''
import argparse
import torch
from datasets import load_dataset
from donut import DonutModel
from transformers import (
DonutImageProcessor,
DonutProcessor,
DonutSwinConfig,
DonutSwinModel,
MBartConfig,
MBartForCausalLM,
VisionEncoderDecoderModel,
XLMRobertaT... | 355 |
'''simple docstring'''
import argparse
import random
import joblib
import numpy as np
import torch
from igf.igf import (
SecondaryLearner,
collect_objective_set,
compute_perplexity,
generate_datasets,
load_gpta,
recopy_gpta,
set_seed,
train_secondary_learner,
)
from torch.utils.dat... | 355 | 1 |
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