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 |
|---|---|---|---|---|
import cmath
import math
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> complex:
"""simple docstring"""
snake_case_ : Optional[int] = math.radians(_UpperCamelCase )
snake_case_ : List[str] ... | 279 |
import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory
from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
convert,
ensure_valid_input,
generate_identified_filename,
infer_... | 279 | 1 |
from __future__ import annotations
def A__ ( lowerCamelCase ) -> int:
UpperCamelCase_: Tuple = len(lowerCamelCase ) // 2
# choose the middle 3 elements
UpperCamelCase_: Union[str, Any] = lst[m - 1 : m + 2]
# if middle element is peak
if three[1] > three[0] and... | 366 |
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mvp import MvpTokenizer
lowerCamelCa... | 223 | 0 |
"""simple docstring"""
import os
import tempfile
import unittest
import uuid
from pathlib import Path
from transformers.testing_utils import get_tests_dir, require_soundfile, require_torch, require_vision
from transformers.tools.agent_types import AgentAudio, AgentImage, AgentText
from transformers.utils impor... | 60 |
"""simple docstring"""
from datetime import datetime
import matplotlib.pyplot as plt
import torch
def _snake_case ( _snake_case : int ):
for param in module.parameters():
lowerCAmelCase : Optional[int] = False
def _snake_case ( ):
lowerCAmelCase : List[str]... | 60 | 1 |
"""simple docstring"""
from bisect import bisect
from itertools import accumulate
def __A ( a_ :Optional[int] , a_ :Any , a_ :List[str] , a_ :int) -> str:
__a : Union[str, Any] = sorted(zip(a_ , a_) , key=lambda a_: x[0... | 188 |
"""simple docstring"""
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class __lowercase ( _UpperCamelCase ):
'''simple docstring'''
__lowerCAmelCase = [''... | 188 | 1 |
"""simple docstring"""
from __future__ import annotations
import sys
from collections import deque
from typing import Generic, TypeVar
__A : List[str] = TypeVar('''T''')
class _UpperCAmelCase ( Generic[T] ):
SCREAMING_SNAKE_CASE_ : Optional... | 33 |
import warnings
from typing import List
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available
class lowerCAmelCase__ ( a):
'''simple docstring''... | 11 | 0 |
'''simple docstring'''
import socket
def lowerCamelCase ( ):
"""simple docstring"""
__magic_name__ : List[str] = socket.socket(socket.AF_INET , socket.SOCK_STREAM )
__magic_name__ : Union[str, Any] = socket.gethostname()
__magic_name__ : int ... | 275 |
'''simple docstring'''
import os
import unittest
from transformers import LxmertTokenizer, LxmertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_to... | 275 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_snake_case = {
'configuration_pix2struct': [
'PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP',
'Pix2StructConfig',
'Pix2Struc... | 250 |
'''simple docstring'''
import doctest
import glob
import importlib
import inspect
import os
import re
from contextlib import contextmanager
from functools import wraps
from unittest.mock import patch
import numpy as np
import pytest
from absl.testing import parameterized
import datasets
from datasets import load_me... | 250 | 1 |
'''simple docstring'''
def __lowerCamelCase ( __snake_case : int ) -> int:
"""simple docstring"""
assert isinstance(_UpperCAmelCase, _UpperCAmelCase ), f"The input value of [n={number}] is not an integer"
if number == 1:
return 2
elif number <... | 350 |
'''simple docstring'''
import tempfile
import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel
from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
... | 136 | 0 |
'''simple docstring'''
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch
if is_vision_available():
from transformers import TvltImageProcessor
if is_speech_... | 331 |
'''simple docstring'''
import os
from typing import Dict, List, Union
import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs
from .tokenization_gpta import GPTaTokenizer
class __lowercase ( tf.keras.layers.Layer ):
def __init__(self , ... | 318 | 0 |
def __UpperCamelCase (_SCREAMING_SNAKE_CASE = 10**12 ) -> int:
lowercase__ = 1
lowercase__ = 0
lowercase__ = 1
lowercase__ = 1
while numerator <= 2 * min_total - 1:
prev_numerator += 2 * numerator
numerator += 2 * prev... | 269 |
import math
import torch
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin
class SCREAMING_SNAKE_CASE (UpperCAmelCase ... | 269 | 1 |
"""simple docstring"""
from __future__ import annotations
def __UpperCAmelCase ( UpperCAmelCase_ : list , UpperCAmelCase_ : int ) -> str:
'''simple docstring'''
if len(UpperCAmelCase_ ) <= 1 or n <= 1:
return
insert_next(UpperCAmelCase_ , ... | 172 | """simple docstring"""
import copy
import inspect
import unittest
from transformers import PretrainedConfig, SwiftFormerConfig
from transformers.testing_utils import (
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch... | 172 | 1 |
"""simple docstring"""
import math
import torch
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin
class lowerCAmelCase__ ( ... | 318 |
"""simple docstring"""
import json
import multiprocessing
import os
import re
from collections import defaultdict
import torch
from accelerate import Accelerator
from accelerate.utils import set_seed
from arguments import HumanEvalArguments
from datasets import load_dataset, load_metric
from torch.utils.d... | 318 | 1 |
"""simple docstring"""
from math import isqrt
def _snake_case ( lowercase__ ):
return all(number % divisor != 0 for divisor in range(2 , isqrt(lowercase__ ) + 1 ) )
def _snake_case ( lowercase__ = 10**6 ):
_lowerCamelCase ... | 96 |
"""simple docstring"""
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
class _lowerCAmelCase ( unittest.TestCase ,lowercase ):
"""simple docstring"""
def _lowercase ( self : List[Any] ):
__lowercase = ... | 17 | 0 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image
from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImagePr... | 365 |
"""simple docstring"""
import argparse
import torch
from transformers import (
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaForAudioFrameClassification,
WavaVecaForSequenceClassification,
WavaVecaForXVector,
logging,
)
logging.set_verbosity_info()
_UpperCamelCase ... | 234 | 0 |
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,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel... | 38 |
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 transformers import (
AutoTokeni... | 257 | 0 |
'''simple docstring'''
import shutil
import tempfile
import unittest
from unittest.mock import patch
from transformers import (
DefaultFlowCallback,
IntervalStrategy,
PrinterCallback,
ProgressCallback,
Trainer,
TrainerCallback,
TrainingArguments,
is_torch_available,
)
from transform... | 275 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
lowerCAmelCase :str = {
'''configuration_vision_encoder_decoder''': ['''VisionEncoderDecoder... | 275 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
_a = {
'configuration_mega': ['MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MegaConfig', 'MegaOnnxConfig'],
}
try:
if not is_torch_availabl... | 17 |
import itertools
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union
import pandas as pd
import pyarrow as pa
import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast
from datasets.utils.py_u... | 199 | 0 |
from maths.prime_check import is_prime
def A ( __UpperCAmelCase ) -> int:
'''simple docstring'''
if not isinstance(__UpperCAmelCase , __UpperCAmelCase ):
UpperCAmelCase_ = f"Input value of [number={number}] must be an integer"
... | 344 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
UpperCamelCase_ = logging.get_logger(__name__)
class a_ ( _snake_case , _snake_case ):
UpperCam... | 344 | 1 |
import argparse
import numpy as np
import torch
from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging
logging.set_verbosity_info()
__lowerCamelCase = logging.get_logger("""transformers.models.speecht5""")
def UpperCamelCase ( __lowerCamelCase : Union[str,... | 59 |
__lowerCamelCase = {
"joule": 1.0,
"kilojoule": 10_00,
"megajoule": 1_00_00_00,
"gigajoule": 10_00_00_00_00,
"wattsecond": 1.0,
"watthour": 36_00,
"kilowatthour": 3_60_00_00,
"newtonmeter": 1.0,
"calorie_nutr": 41_86.8,
"kilocalorie_nutr": 4_18_68_00.00,
... | 59 | 1 |
import argparse
import os
from pathlib import Path
import fairseq
import torch
from packaging import version
from torch import nn
from transformers import (
BartConfig,
BartForConditionalGeneration,
BartForSequenceClassification,
BartModel,
BartTokenizer,
)
from transformers.utils import logging
... | 367 |
'''simple docstring'''
import inspect
import unittest
from huggingface_hub import hf_hub_download
from transformers import ConvNextConfig, UperNetConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import is_torch_availab... | 229 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
__UpperCAmelCase = {
"""configuration_trocr""": ["""TROCR_PRETRAINED_CONFIG_ARC... | 323 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
"""microsoft/git-base""": """https://huggingface.co/microsoft/gi... | 323 | 1 |
A_ :Union[str, Any] = [
'''DownloadConfig''',
'''DownloadManager''',
'''DownloadMode''',
'''StreamingDownloadManager''',
]
from .download_config import DownloadConfig
from .download_manager import DownloadManager, DownloadMode
from .streaming_download_manager import St... | 245 |
def A ( a_ ) -> bool:
if number < 0:
raise ValueError('number must not be negative' )
return number & (number - 1) == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 245 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : Any = logging.get_logger(__name__)
__A : int = {
'''EleutherAI/gpt-neox-20b''': '''https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json''',
# See all GPTNeoX models at h... | 138 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_A = {
"configuration_distilbert": [
"DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP",
"... | 231 | 0 |
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 tokeni... | 252 |
import argparse
import shlex
import runhouse as rh
if __name__ == "__main__":
# Refer to https://runhouse-docs.readthedocs-hosted.com/en/latest/api/python/cluster.html#hardware-setup for cloud access
# setup instructions, if using on-demand hardware
# If user passes --user <user> --host <ho... | 252 | 1 |
from __future__ import annotations
def _UpperCamelCase ( lowercase__ , lowercase__ ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = sorted(numsa + numsa )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Tuple = divmod(len(_lowerCAmelCa... | 9 |
"""simple docstring"""
from collections import deque
from math import floor
from random import random
from time import time
class __lowerCamelCase :
'''simple docstring'''
def __init__( self ) -> Tuple:
_a = {}
def _UpperCAmelCase ( s... | 320 | 0 |
# Note: if you intend to run this script make sure you look under scripts/fsmt/
# to locate the appropriate script to do the work correctly. There is a set of scripts to:
# - download and prepare data and run the conversion script
# - perform eval to get the best hparam into the config
# - generate model_cards -... | 359 |
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onn... | 138 | 0 |
'''simple docstring'''
import math
import torch
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin
class __lowercase ( _lowercase , _lowe... | 318 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import XLMRobertaTokenizer
from diffusers import (
AltDiffusionImgaImgPipeline,
AutoencoderKL,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProces... | 318 | 1 |
'''simple docstring'''
import warnings
from typing import Dict
import numpy as np
from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF_MODEL_FO... | 352 |
'''simple docstring'''
import logging
from transformers import PretrainedConfig
_snake_case = logging.getLogger(__name__)
_snake_case = {
'bertabs-finetuned-cnndm': 'https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json',
}
class... | 199 | 0 |
import math
def lowerCAmelCase__ ( a__: int ) -> str:
'''simple docstring'''
_UpperCAmelCase = 0
_UpperCAmelCase = 0
while num > 0:
_UpperCAmelCase = num % 8
_UpperCAmelCase = octal + (remainder * math.floor(math.pow(1_... | 329 |
import os
import tempfile
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from torch import nn
from transformers import (
Adafactor,
AdamW,
get_constant_schedule,
get_constant_sche... | 329 | 1 |
"""simple docstring"""
import warnings
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_UpperCamelCase : Union[str, Any] = logging.get_... | 351 |
"""simple docstring"""
import os
import sys
_UpperCamelCase : str = os.path.join(os.path.dirname(__file__), 'src')
sys.path.append(SRC_DIR)
from transformers import (
AutoConfig,
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForQuestionAnswering,
Auto... | 186 | 0 |
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,
MobileViTVaForImageClassification,
MobileViTVaFor... | 295 |
from collections.abc import Sequence
def _lowerCamelCase( lowercase__ , lowercase__ = False ) -> float:
'''simple docstring'''
if not arr:
return 0
__lowercase= 0 if allow_empty_subarrays else float('-inf' )
__lowercase= 0.0
for num in arr:
__lowercase= max(0 if ... | 295 | 1 |
'''simple docstring'''
import itertools
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union
import pandas as pd
import pyarrow as pa
import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast
fr... | 199 |
'''simple docstring'''
import warnings
from typing import List
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available
class a__ ( lowerCamelCase_ ):
_SCREAM... | 199 | 1 |
'''simple docstring'''
from maths.prime_check import is_prime
def UpperCAmelCase ( a_ ) -> int:
"""simple docstring"""
if not isinstance(a_ , a_ ):
A_ : int = F"Input value of [number={number}] must be an integer"
raise... | 344 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
UpperCamelCase__ : Any = {
'configuration_data2vec_audio': ['DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Data2... | 344 | 1 |
import unittest
from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch
class lowercase__ ( unittest.TestCase ):
'''simple docstring'''
a : Union[str, Any] = JukeboxTokenizer
a : Optional[int] ... | 365 |
import argparse
import logging
import os
import datasets
import tensorflow as tf
from transformers import AutoTokenizer
UpperCAmelCase_ = logging.getLogger(__name__)
def lowerCAmelCase_ ( ) -> Any:
UpperCamelCase__ : Dict = argparse.Arg... | 247 | 0 |
'''simple docstring'''
import random
from typing import Any
def SCREAMING_SNAKE_CASE_ (UpperCamelCase ) -> Union[str, Any]:
for _ in range(len(a__ ) ):
lowerCamelCase__ : Tuple = random.randint(0 ... | 41 |
from __future__ import annotations
def _UpperCAmelCase ( a__):
'''simple docstring'''
a_ : List[str] = str(a__)
return len(a__) == 9 and set(a__) == set("""123456789""")
def _UpperCAmelCase ( ):
'''simple docstring'''
for base_num in range(9_9_9_9 , 4_9... | 248 | 0 |
from __future__ import annotations
import typing
from collections.abc import Iterable
import numpy as np
_lowerCamelCase : Optional[Any] = typing.Union[Iterable[float], Iterable[int], np.ndarray] # noqa: UP007
_lowerCamelCase : Optional[int] = typing.Union[np.floataa, i... | 360 |
def a__ ( UpperCAmelCase : int ) -> bool:
if not isinstance(UpperCAmelCase , UpperCAmelCase ):
UpperCAmelCase : List[str] = f'''Input value of [number={number}] must be an integer'''
raise TypeError(UpperCAmelCase )
if number < 0:
return Fal... | 99 | 0 |
import os
import zipfile
import pytest
from datasets.utils.extract import (
BzipaExtractor,
Extractor,
GzipExtractor,
LzaExtractor,
SevenZipExtractor,
TarExtractor,
XzExtractor,
ZipExtractor,
ZstdExtractor,
)
from .utils import require_lza, require_pyazr, require_zstandard
@p... | 348 | from dataclasses import asdict, dataclass
from typing import Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__snake_case = logging.get_logger(__name__)
# TODO Update this
__snake_case = {
'''facebook/esm-1b''': '''https://huggingface.co/facebook/esm-1b/re... | 348 | 1 |
from datetime import datetime
import requests
from bsa import BeautifulSoup
if __name__ == "__main__":
__lowerCamelCase : Tuple = input('''Enter image url: ''').strip()
print(F"""Downloading image from {url} ...""")
__lowerCamelCase : Optional[Any] = Beautifu... | 204 | import unittest
from .lib import (
Matrix,
Vector,
axpy,
square_zero_matrix,
unit_basis_vector,
zero_vector,
)
class __snake_case ( unittest.TestCase ):
def __a ( self : Dict ):
"""simple docstring"""
... | 204 | 1 |
'''simple docstring'''
import math
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : int ):
'''simple docstring'''
UpperCAmelCase__ = 0
UpperCAmelCase__ = 0
while num > 0:
UpperCAmelCase__ = num % 8
UpperCAmelCase__ = octal + (remainder * math.flo... | 346 |
'''simple docstring'''
from datasets.utils.patching import _PatchedModuleObj, patch_submodule
from . import _test_patching
def _UpperCamelCase ( ):
'''simple docstring'''
import os as original_os
from os import path as original_path
from os import rename as original_rename
from os.path im... | 346 | 1 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
if is_torch_available():
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
@require_torch
@require_sentencepiece
@... | 265 |
import pprint
import requests
_A : Any = 'https://zenquotes.io/api'
def _a ( ) -> list:
"""simple docstring"""
return requests.get(API_ENDPOINT_URL + '''/today''' ).json()
def _a ( ) -> list:
"""simple docstring"""
return requests.get(API_EN... | 265 | 1 |
'''simple docstring'''
a_ = '\n# Installazione di Transformers\n! pip install transformers datasets\n# Per installare dalla fonte invece dell\'ultima versione rilasciata, commenta il comando sopra e\n# rimuovi la modalità commento al comando seguente.\n# ! pip install git+https://github.com/huggi... | 152 |
'''simple docstring'''
import os
from bleurt import score # From: git+https://github.com/google-research/bleurt.git
import datasets
a_ = datasets.logging.get_logger(__name__)
a_ = '\\n@inproceedings{bleurt,\n title={BLEURT: Learning Robust Metrics for Text Generation},\n... | 152 | 1 |
"""simple docstring"""
import argparse
import logging
import os
import re
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
DataCollatorForLanguageModeling,
PushToHubCallback,
TFAutoModelForMaskedLM,
create_optimizer,
)
__UpperCamelCase = logging.ge... | 367 | """simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_out... | 312 | 0 |
'''simple docstring'''
import itertools
from dataclasses import dataclass
from typing import Optional
import pandas as pd
import pyarrow as pa
import datasets
from datasets.table import table_cast
@dataclass
class __SCREAMING_SNAKE_CASE ( datasets.BuilderConfig ):
snake_c... | 152 |
import os
def _lowercase ( ) -> List[str]:
'''simple docstring'''
with open(os.path.dirname(UpperCamelCase_ ) + '/p022_names.txt' ) as file:
SCREAMING_SNAKE_CASE__ = str(file.readlines()[0] )
SCREAMING_SNAKE_CASE__ = names.replace('"' ... | 176 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, PreTrainedTokenize... | 370 |
"""simple docstring"""
def _lowerCamelCase ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ):
'''simple docstring'''
__lowerCAmelCase = [False] * len(_UpperCamelCase )
__lowerCAmelCase = []
queue.append(_UpperCamelCase )
__... | 259 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available
UpperCamelCase__ : str = {'''tokenization_herbert''': ['''HerbertTokenizer''']}
try:
if not is_tokenizers_available():
raise OptionalDe... | 112 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase__ : Any = {
'''configuration_instructblip''': [
'''INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''InstructBlipConfig''',
... | 112 | 1 |
def lowerCamelCase_ ( _a : int , _a : int ):
'''simple docstring'''
if b == 0:
return 1
if (b % 2) == 0:
return actual_power(_a , int(b / 2 ) ) * actual_power(_a , int(b / 2 ) )
else:
return a * actual_power(_a , int(b / 2 ) ... | 59 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
UpperCamelCase_ = logging.get_logger(__name__)
UpperCamelCase_ = {
'''shi-labs/dinat-mini-in1k-224''': '''http... | 59 | 1 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configurat... | 134 |
'''simple docstring'''
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 Model... | 134 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
lowerCAmelCase_ : Tuple = {'''configuration_encoder_decoder... | 350 |
"""simple docstring"""
def _lowerCAmelCase ( ):
'''simple docstring'''
return [
a * b * (1000 - a - b)
for a in range(1 , 999 )
for b in range(lowerCAmelCase , 999 )
if (a * a + b * b == (1000 - a - b) ** 2)
][0]
if ... | 248 | 0 |
'''simple docstring'''
from typing import List
import datasets
from datasets.tasks import AudioClassification
from ..folder_based_builder import folder_based_builder
lowercase__ : str = datasets.utils.logging.get_logger(__name__)
class __lowerCAmelCase ( folder_based_builder.Folder... | 324 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
impor... | 291 | 0 |
import colorsys
from PIL import Image # type: ignore
def lowerCAmelCase_ ( snake_case_,snake_case_,snake_case_ ):
_A : Dict = x
_A : Optional[Any] = y
for step in range(snake_case_ ): # noqa: B007
_A : Lis... | 343 |
from __future__ import annotations
from decimal import Decimal
from numpy import array
def lowerCAmelCase_ ( snake_case_ ):
_A : Tuple = Decimal
# Check if the provided matrix has 2 rows and 2 columns
# since this implementation only works for 2x2 mat... | 343 | 1 |
'''simple docstring'''
import math
import os
import re
import sys
import unittest
from pathlib import Path
from typing import Tuple
from unittest.mock import patch
from parameterized import parameterized
from transformers.testing_utils import (
CaptureStderr,
ExtendSysPath,
TestCa... | 3 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
snake_case_ = {
"""configuration_longt5""": ["""LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LongT5Config""", """LongT5On... | 78 | 0 |
"""simple docstring"""
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_tf
if is_tf_available():
import tensorflow as tf
from tensorflow.python.eager import context
from tensorflow.python.framework import ops
from transformers impo... | 95 | """simple docstring"""
import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
_a : Dict= logging.get_logger(__name__)
class UpperCamelCase ( lowercase ):
def __init__(self : List[str] , *_A : Dict ... | 95 | 1 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
snake_case_ = {
'''configuration_mctct''': ['''MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MCTCTConfig'''],
'''feature_extraction_mctct''': ['''MCTCTFeatureExtractor'''],
'''process... | 214 |
def __lowerCamelCase ( UpperCAmelCase_ : int = 100_0000 ):
"""simple docstring"""
a :Any = set(range(3 , UpperCAmelCase_ , 2 ) )
primes.add(2 )
for p in range(3 , UpperCAmelCase_ , 2 ):
if p not in primes:
... | 94 | 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,
)
_SCREAMING_SNAKE_CASE = {
"con... | 355 |
'''simple docstring'''
import argparse
import json
from collections import OrderedDict
import torch
from huggingface_hub import cached_download, hf_hub_url
from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification
def __lowerCamelCase ( __lowerCA... | 3 | 0 |
def SCREAMING_SNAKE_CASE_ ( __A : int , __A : Any ) -> str:
"""simple docstring"""
return abs(lowerCamelCase__ ) if a == 0 else greatest_common_divisor(b % a , lowerCamelCase__ )
def SCREAMING_SNAKE_CASE_ ( __A : List[str] , ... | 32 |
'''simple docstring'''
from math import pow
def a ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , ):
'''simple docstring'''
if current_sum == needed_sum:
# If the sum of the powers is equal ... | 206 | 0 |
from argparse import ArgumentParser
from datasets.commands.convert import ConvertCommand
from datasets.commands.dummy_data import DummyDataCommand
from datasets.commands.env import EnvironmentCommand
from datasets.commands.run_beam import RunBeamCommand
from datasets.commands.test import TestCommand
... | 238 |
from __future__ import annotations
snake_case_ = 'Muhammad Umer Farooq'
snake_case_ = 'MIT'
snake_case_ = '1.0.0'
snake_case_ = 'Muhammad Umer Farooq'
snake_case_ = 'contact@muhammadumerfarooq.me'
snake_case_ = ... | 238 | 1 |
"""simple docstring"""
import unittest
from transformers import MraConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_att... | 61 |
from unittest import TestCase
from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters
def __snake_case ( ):
"""simple docstring"""
A_ = {
"repo_name": ["test_repo1", "test_repo2", "test_repo3"],
"... | 312 | 0 |
'''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.org/... | 104 |
'''simple docstring'''
import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def _A () -> Optional[Any]:
'''simple docstr... | 104 | 1 |
import copy
import tempfile
import unittest
from huggingface_hub import HfFolder, delete_repo
from parameterized import parameterized
from requests.exceptions import HTTPError
from transformers import AutoConfig, GenerationConfig
from transformers.testing_utils import TOKEN, USER, is_staging_te... | 29 |
from math import factorial
__snake_case = {str(digit): factorial(digit) for digit in range(10)}
def _A ( SCREAMING_SNAKE_CASE__ : int ):
if not isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
raise TypeError('''Parameter number must be int''' ... | 259 | 0 |
"""simple docstring"""
def UpperCAmelCase__ (lowerCAmelCase_ ):
'''simple docstring'''
return str(lowerCAmelCase_ ) == str(lowerCAmelCase_ )[::-1]
def UpperCAmelCase__ (lowerCAmelCase_ ):
'''simple docstring'''
return int(lowerCAmelCase... | 195 |
"""simple docstring"""
import argparse
import re
from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints
from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pyt... | 195 | 1 |
"""simple docstring"""
import math
def lowerCamelCase__ ( _lowerCamelCase : Any ) -> Union[str, Any]:
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, ... | 183 |
"""simple docstring"""
# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile
from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
fro... | 96 | 0 |
import torch
from diffusers import DDPMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class __lowercase (__SCREAMING_SNAKE_CASE ):
"""simple docstring"""
_UpperCAmelCase = (DDPMParallelScheduler,)
... | 162 |
import argparse
import os
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_task_guides.py
lowerCAmelCase__ : Optional[Any] ='src/transformers'
lowerCAmelC... | 162 | 1 |
'''simple docstring'''
import unittest
from transformers import AlbertConfig, is_torch_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_... | 321 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class SCREAMING_SNAKE_CASE_ ( metaclass=__a ):
"""simple docstring"""
__lowercase : Tuple = ['''keras_nlp''']
def __init__( self , *lowerCAmelCase__ , **lowerCAmelCase__):
... | 100 | 0 |
import json
import os
from dataclasses import dataclass
from functools import partial
from typing import Callable
import flax.linen as nn
import jax
import jax.numpy as jnp
import joblib
import optax
import wandb
from flax import jax_utils, struct, traverse_util
from flax.serialization impor... | 368 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A_ :int = logging.get_logger(__name__)
A_ :List[str] = {
'''facebook/xlm-roberta-xl''': ''... | 245 | 0 |
import math
def _SCREAMING_SNAKE_CASE ( a ) -> list[int]:
__A : List[str] = []
__A : Any = 2
__A : Union[str, Any] = int(math.sqrt(a ) ) # Size of every segment
__A : Any = [True] * (end + 1)
__A : Lis... | 280 |
import unittest
from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
UpperCAmelCase : List[str] = ... | 280 | 1 |
'''simple docstring'''
import argparse
import io
import requests
import torch
from omegaconf import OmegaConf
from diffusers import AutoencoderKL
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import (
assign_to_checkpoint,
conv_attn_to_linear,
create_vae_diffu... | 190 |
'''simple docstring'''
import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image
if version.parse(version.parse(PIL.__version__).base_version) >= version.parse('''9.1.0'''):
lowercase__ : Dict = {
'''linear''': PIL.Image.Resampling.BILINE... | 190 | 1 |
'''simple docstring'''
import io
import json
import unittest
from parameterized import parameterized
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu
a : Any ... | 56 |
'''simple docstring'''
import re
from filelock import FileLock
try:
import nltk
a : Union[str, Any] = True
except (ImportError, ModuleNotFoundError):
a : Any = False
if NLTK_AVAILABLE:
with FileLock('.lock') as lock:
nltk.download('punkt', q... | 56 | 1 |
"""simple docstring"""
import torch
def lowerCAmelCase( )-> str:
"""simple docstring"""
if torch.cuda.is_available():
UpperCamelCase_ = torch.cuda.device_count()
else:
UpperCamelCase_ = 0
print(f"Successfully ran on {num... | 366 |
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ = 1_0_0 )-> int:
"""simple docstring"""
UpperCamelCase_ = (n * (n + 1) // 2) ** 2
UpperCamelCase_ = n * (n + 1) * (2 * n + 1) // 6
return sum_cubes - sum_squares
if __name__ == "__main__":
print(F'''{solutio... | 60 | 0 |
def SCREAMING_SNAKE_CASE_ ( snake_case__ ) -> List[Any]:
if divisor % 5 == 0 or divisor % 2 == 0:
return 0
lowerCAmelCase = 1
lowerCAmelCase = 1
while repunit:
lowerCAmelCase = (1_0 * repunit + 1) % divisor
... | 338 |
'''simple docstring'''
import pytest
import datasets.config
from datasets.utils.info_utils import is_small_dataset
@pytest.mark.parametrize('''dataset_size''' , [None, 400 * 2**20, 600 * 2**20] )
@pytest.mark.parametrize('''input_in_memory_max_size''' , ['''default''', 0, 100... | 3 | 0 |
"""simple docstring"""
from math import factorial
_UpperCamelCase = {str(d): factorial(d) for d in range(10)}
def _a ( _snake_case ):
"""simple docstring"""
return sum(DIGIT_FACTORIAL[d] for d in str(_snake_case ) )
def _a ( ):
""... | 234 |
"""simple docstring"""
import argparse
import torch
from transformers import (
SpeechTaConfig,
SpeechTaFeatureExtractor,
SpeechTaForSpeechToSpeech,
SpeechTaForSpeechToText,
SpeechTaForTextToSpeech,
SpeechTaProcessor,
SpeechTaTokenizer,
logging,
)
from transformers.tokeniz... | 234 | 1 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Iterator
from typing import Generic, TypeVar
_A : str =TypeVar('''T''')
class _lowercase ( Generic[T] ):
def __init__( self: List[str] , UpperCamelC... | 41 |
import random
def lowerCAmelCase__ ( _SCREAMING_SNAKE_CASE : list , _SCREAMING_SNAKE_CASE : List[Any] ):
"""simple docstring"""
__a , __a , __a = [], [], []
for element in data:
if element < pivot:
less.append(_SCREAMING_SNAKE_CASE ... | 302 | 0 |
'''simple docstring'''
def __lowerCamelCase ( __snake_case : int | float | str ) -> tuple[int, int]:
"""simple docstring"""
try:
A__ : List[str] =float(__snake_case )
except ValueError:
raise ValueError("""Please enter a vali... | 357 |
'''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,
LMSDiscreteScheduler,
... | 136 | 0 |
from __future__ import annotations
class _A :
def __init__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[Any] = text, pattern
SCREAMING_SNAKE_CASE_ : List[str] ... | 253 |
from __future__ import annotations
import math
import numpy as np
from numpy.linalg import norm
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase, _UpperCAmelCase ) -> float:
'''simple docstring'''
return math.sqrt(sum(pow(a - b, 2 ) for a, b in zip(_UpperCAmelCase, _Upp... | 138 | 0 |
"""simple docstring"""
def a__ ( _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
for i in range(len(_SCREAMING_SNAKE_CASE ) - 1 , 0 , -1 ):
UpperCamelCase = False
for j in range(_SCREAMING_SNAKE_CASE , 0 , -1 ):
if unsorted[j] < unsor... | 358 |
"""simple docstring"""
import doctest
from collections import deque
import numpy as np
class _lowerCamelCase :
def __init__(self ) -> None:
UpperCamelCase = [2, 1, 2, -1]
UpperCamelCase = [1, 2, 3, 4]
def snake_case_ (self ) -> list... | 244 | 0 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, BlipaProcessor, BlipI... | 59 |
import os
import shutil
from pathlib import Path
from typing import Optional, Union
import numpy as np
from huggingface_hub import hf_hub_download
from ..utils import ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME, is_onnx_available, logging
if is_onnx_available():
import onnxruntime as ort
__l... | 59 | 1 |
import numpy as np
import skfuzzy as fuzz
if __name__ == "__main__":
# Create universe of discourse in Python using linspace ()
__UpperCAmelCase = np.linspace(start=0, stop=75, num=75, endpoint=True, retstep=False)
# Create two fuzzy sets by defining any membership function
... | 366 |
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():
... | 139 | 0 |
"""simple docstring"""
import argparse
import json
import re
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileNetVaConfig,
MobileNetVaForImageClassification,
MobileNetV... | 269 |
"""simple docstring"""
import os
from math import logaa
def _lowercase ( __snake_case = "base_exp.txt" ) -> int:
__lowerCAmelCase : float = 0
__lowerCAmelCase : Any = 0
for i, line in enumerate(open(os.path.join(os.path.d... | 269 | 1 |
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(_... | 119 | from __future__ import annotations
lowerCAmelCase__ = tuple[int, int, int]
lowerCAmelCase__ = tuple[str, str, str]
# used alphabet --------------------------
# from string.ascii_uppercase
lowerCAmelCase__ = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ'
# -------------------------- default selecti... | 119 | 1 |
import importlib
import os
import sys
# This is required to make the module import works (when the python process is running from the root of the repo)
sys.path.append('.')
def snake_case( __magic_name__ ) -> List[Any]:
'''simple docstring'''
low... | 308 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowerCAmelCase_ = {
'configuration_mask2former': [
'MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'Mask2FormerConfig',
],
}
tr... | 308 | 1 |
'''simple docstring'''
from __future__ import annotations
def _snake_case ( A , A = None ) -> list[list[str]]:
lowerCAmelCase__ = word_bank or []
# create a table
lowerCAmelCase__ = len(A ) + 1
lowerCAmelCase__ ... | 228 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_... | 228 | 1 |
'''simple docstring'''
from typing import List
import datasets
from datasets.tasks import AudioClassification
from ..folder_based_builder import folder_based_builder
__a = datasets.utils.logging.get_logger(__name__)
class UpperCAmelCase_ ( folder_based_builder.FolderBasedBu... | 35 |
import argparse
import shutil
from pathlib import Path
from tqdm import tqdm
from transformers import AutoTokenizer
def lowerCAmelCase_ ( _lowercase : Dict , _lowercase : str , _lowercase : str , _lowercase : Optional[Any]=1024) -> List[Any]:... | 170 | 0 |
'''simple docstring'''
import warnings
from .state import AcceleratorState, GradientState
warnings.filterwarnings("ignore", category=UserWarning, module="torch.optim.lr_scheduler")
class a :
def __init__( self , __magic_name__ , __magic_name__ , __magic_name__ = True ,... | 363 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
a_ : List[Any] = {"configuration_gpt_neox": ["GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_M... | 104 | 0 |
def __lowerCAmelCase ( a__ , a__ , a__ ) -> int:
def count_of_possible_combinations(a__ ) -> int:
if target < 0:
return 0
if target == 0:
return 1
return sum(count_of_possible_combinations(target - item ) for item in array )
r... | 6 | '''simple docstring'''
def __UpperCamelCase ( UpperCAmelCase ):
lowercase__ , lowercase__ : Optional[int] = [], []
while len(UpperCAmelCase ) > 1:
lowercase__ , lowercase__ : List[str] = min(UpperCAmelCase ), max(UpperCAmelCase )
start.append(UpperCAme... | 198 | 0 |
"""simple docstring"""
import multiprocessing
from typing import TYPE_CHECKING, Optional, Union
from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream
if TYPE_CHECKING:
... | 80 |
"""simple docstring"""
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union
# Build the list of all image processors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trus... | 80 | 1 |
def _UpperCamelCase ( lowercase__ = 1000 ):
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Optional[Any] = 1, 1
__SCREAMING_SNAKE_CASE : List[str] = []
for i in range(1 , n + 1 ):
__SCREAMING_SNAKE_CASE : O... | 9 |
import os
import torch
from ..logging import get_logger
from .constants import FSDP_PYTORCH_VERSION, MODEL_NAME, OPTIMIZER_NAME
from .versions import is_torch_version
if is_torch_version(""">=""", FSDP_PYTORCH_VERSION):
import torch.distributed.checkpoint as dist_cp
from torch.distributed.checkpoint.... | 92 | 0 |
"""simple docstring"""
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase ):
return numa ^ numa < 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 316 |
"""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,
)
A_ : int = {
"configuration_clip": ... | 316 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a = logging.get_logger(__name__)
a = {
'uclanlp/visualbert-vqa': 'https://huggingface.co/uclanlp/visualbert-vqa/resolve/main/config.json',
'uclanlp/visualbert-... | 155 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_tf_available,
is_torch_available,
)
a = {
'configuration_speech_to_text': ['SPEECH_T... | 155 | 1 |
"""simple docstring"""
import math
import time
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
import torch_xla.debug.metrics as met
class ... | 355 | """simple docstring"""
def lowercase_ ( _lowerCamelCase: int ) -> int:
'''simple docstring'''
if not isinstance(_lowerCamelCase , _lowerCamelCase ):
raise TypeError("Input value must be an 'int' type" )
__lowerCamelCase : Dict = 0
... | 64 | 0 |
def __A ( __lowerCAmelCase )-> int:
"""simple docstring"""
if not isinstance(__lowerCAmelCase , __lowerCAmelCase ):
raise ValueError('multiplicative_persistence() only accepts integral values' )
if num < 0:
raise ValueError('multipli... | 39 |
import torch
from diffusers import CMStochasticIterativeScheduler
from .test_schedulers import SchedulerCommonTest
class __A( __lowerCamelCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = (CMStochasticIterativeScheduler,)
SCREAMING_SNAKE_CASE__ = 10
def UpperCAmel... | 244 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
_snake_case = {
"configuration_convnext": ["CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ConvNextConfig", "ConvNextOnnxConfig"]
}... | 343 |
# Copyright (c) 2021-, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by... | 343 | 1 |
'''simple docstring'''
import argparse
import json
import numpy
import torch
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def a_ ( _lowerCAmelCase... | 208 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_UpperCamelCase = logging.get_logger(__name__)
_UpperCamelCase =... | 208 | 1 |
"""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 (
ChannelDi... | 53 |
"""simple docstring"""
from typing import Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, ... | 53 | 1 |
'''simple docstring'''
import os
import jsonlines
import numpy as np
from tqdm import tqdm
__a: Optional[int] = 20_48
__a: Union[str, Any] = 40_96
__a: List[Any] = 42
__a: List[Any] = os.environ.pop("""PROCESS_TRAIN""", """false""")
__a: Optional[Any] = {"""nul... | 198 | '''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 convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
OPENA... | 198 | 1 |
from statistics import mean
import numpy as np
def lowerCamelCase ( a_ , a_ , a_ , a_ ) -> list:
lowerCAmelCase_ = 0
# Number of processes finished
lowerCAmelCase_ = 0
# Displays the finished process.... | 14 |
import baseaa
def lowerCamelCase ( a_ ) -> bytes:
return baseaa.baaencode(string.encode('utf-8' ) )
def lowerCamelCase ( a_ ) -> str:
return baseaa.baadecode(a_ ).decode('utf-8' )
if __name__ == "__main__":
lowerCamelC... | 14 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ :Any = logging.get_logger(__name__)
A_ :int = {
'''sayakpaul/vit-msn-base''': '''https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json''',
# See all ViT... | 71 |
A_ :str = '''0.21.0'''
from .accelerator import Accelerator
from .big_modeling import (
cpu_offload,
cpu_offload_with_hook,
disk_offload,
dispatch_model,
init_empty_weights,
init_on_device,
load_checkpoint_and_dispatch,
)
from .data_loader import s... | 71 | 1 |
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> float:
UpperCamelCase__ : List[str] = (num_of_terms / 2) * (2 * first_term + (num_of_terms - 1) * common_diff)
# formula for sum of series
return total
... | 196 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase , __lowerCAmelCase = None , __lowerCAmelCase = None ) -> None:
if start is None:
UpperCamelCase__ : Union[str, Any] = 0
if end is None:
U... | 196 | 1 |
'''simple docstring'''
import colorsys
from PIL import Image # type: ignore
def _UpperCamelCase ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
UpperCAmelCase__ : Dict = x
UpperCAmelCase__ : int = y
... | 163 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable
lowerCAmelCase__ = {
'''configuration_gpt_neox_japanese''': ['''GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTNeoXJap... | 104 | 0 |
"""simple docstring"""
from typing import Any
class __snake_case :
def __init__( self , lowercase) -> List[Any]:
'''simple docstring'''
a__: Tuple = data
a__: Optional[int] = None
def __repr__( ... | 353 | """simple docstring"""
import argparse
import collections
import json
import os
import re
import string
import sys
import numpy as np
lowercase__ = re.compile(r'\b(a|an|the)\b', re.UNICODE)
lowercase__ = None
def __a ( ) ->List[Any]:
a__: Dict = argparse.Ar... | 203 | 0 |
import os
from distutils.util import strtobool
def _snake_case ( lowerCAmelCase : Dict , lowerCAmelCase : Tuple ):
"""simple docstring"""
for e in env_keys:
SCREAMING_SNAKE_CASE_ : Dict = int(os.environ.get(lowerCAmelCase , -1 ) )
if val >= 0:
retur... | 18 |
'''simple docstring'''
def lowerCAmelCase_ ( _lowerCamelCase: list ):
if any(not isinstance(_lowerCamelCase , _lowerCamelCase ) or x < 0 for x in sequence ):
raise TypeError("""Sequence must be list of non-negative integers""" )
for _ in range(len(_lowerCamelCase )... | 112 | 0 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"""Intel/dpt-large""": """https://huggingface.co/Intel/dpt-large/resolve/main/config.json""",
# See all ... | 359 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging
if TYPE_CHECKING:
from ...feature_extraction_utils import FeatureEx... | 224 | 0 |
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