code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
'''simple docstring'''
# Usage:
# ./gen-card-allenai-wmt16.py
import os
from pathlib import Path
def __lowerCamelCase ( A__ , A__ , A__ , A__ ) -> Union[str, Any]:
"""simple docstring"""
UpperCamelCase = {
'en': ... | 28 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@datacl... | 28 | 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
lowerCamelCase_ : int = 4
lowerCamelCase_ : Optional[Any] = 3
class a__ (... | 197 | 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
from diffusers.util... | 197 | 1 |
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ : Dict = logging.get_logger(__name__)
lowercase__ : List[Any] = {
"RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json",
}
class ... | 187 |
from timeit import timeit
lowercase__ : Union[str, Any] = {
"MALAYALAM": True,
"String": False,
"rotor": True,
"level": True,
"A": True,
"BB": True,
"ABC": False,
"amanaplanacanalpanama": True, # "a man a plan a canal panama"
}
# Ensure our test data... | 187 | 1 |
"""simple docstring"""
def _lowerCamelCase(__UpperCamelCase = 100 ) -> str:
_lowerCAmelCase =n * (n + 1) * (2 * n + 1) / 6
_lowerCAmelCase =(n * (n + 1) / 2) ** 2
return int(square_of_sum - sum_of_squares )
if __name__ == "__main__":
print(F"""{solution() = }""")
| 350 |
"""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, DDPMScheduler, StableDiffusionUpscalePipeline, UNetaDConditionM... | 341 | 0 |
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union
# Build the list of all image processors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...image_pr... | 313 |
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__ : Any = logging.... | 313 | 1 |
'''simple docstring'''
import os
def __a ( UpperCAmelCase = "matrix.txt" ) ->int:
"""simple docstring"""
with open(os.path.join(os.path.dirname(UpperCAmelCase ) , UpperCAmelCase ) ) as in_file:
A = in_file.read()
A = [[int(UpperCAmel... | 337 |
'''simple docstring'''
from typing import List, Optional, Union
import numpy as np
import tensorflow as tf
from .utils import logging
_lowerCamelCase : List[Any] = logging.get_logger(__name__)
def __a ( UpperCAmelCase ) ->List[int]:
"""simple docstring"""
if isin... | 337 | 1 |
def UpperCamelCase ( __lowercase : Optional[Any] ):
'''simple docstring'''
if upper_limit < 0:
raise ValueError('Limit for the Catalan sequence must be ≥ 0' )
A_ : Optional[int] = [0] * (upper_limit + 1)
# Base case: C(0) = C(1) = 1
A_ : Tuple = ... | 140 |
"""simple docstring"""
from collections import Counter
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
lowerCAmelCase__ = datasets.load_iris()
lowerCAmelCase__ = np.array(data['''data'''])
lowerCAmelCase__ = np.array(data['''target'''])
lowerCA... | 153 | 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,... | 356 |
'''simple docstring'''
from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import pyarrow as pa
if TYPE_CHECKING:
from .features import FeatureType
@dataclass
class _A :
_SCREAMING_SNAKE_CASE : List[str]
_... | 16 | 0 |
"""simple docstring"""
import random
import sys
import numpy as np
from matplotlib import pyplot as plt
from matplotlib.colors import ListedColormap
__lowerCAmelCase : Optional[int] ="""Usage of script: script_name <size_of_canvas:int>"""
__lowerCAmelCase : List[Any] ... | 197 | """simple docstring"""
from scipy.stats import pearsonr
import datasets
__lowerCAmelCase : List[Any] ="""
Pearson correlation coefficient and p-value for testing non-correlation.
The Pearson correlation coefficient measures the linear relationship between two datasets. The calcula... | 197 | 1 |
import unittest
import torch
from torch import nn
from diffusers.models.activations import get_activation
class snake_case__ (unittest.TestCase ):
"""simple docstring"""
def SCREAMING_SNAKE_CASE__( self ) -> Dict:
"""simple docs... | 361 |
import argparse
import json
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accel... | 266 | 0 |
'''simple docstring'''
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 lowerCamelCase ( lowerCAmelCase : Any ):
"""simple docstring"""
__magic_name__ ... | 331 |
'''simple docstring'''
def lowerCamelCase ( ):
"""simple docstring"""
return 1
def lowerCamelCase ( lowerCAmelCase : int ):
"""simple docstring"""
return 0 if x < 0 else two_pence(x - 2 ) + one_pence()
def lowerCamelCase ( lowerCAmelCase : int ):
"""s... | 331 | 1 |
import importlib.metadata
from typing import Union
from packaging.version import Version, parse
from .constants import STR_OPERATION_TO_FUNC
_UpperCamelCase = parse(importlib.metadata.version('''torch'''))
def UpperCamelCase_( snake_case__: Union[str, Version] , snake_case__: ... | 335 |
from multiprocessing import Lock, Pipe, Process
# lock used to ensure that two processes do not access a pipe at the same time
_UpperCamelCase = Lock()
def UpperCamelCase_( snake_case__: Optional[Any] , snake_case__: Optional[int] , snake_case__: Tuple , snake_case__: Tuple ... | 335 | 1 |
import os
def __lowercase ( _UpperCamelCase = "matrix.txt" ) ->int:
"""simple docstring"""
with open(os.path.join(os.path.dirname(_UpperCamelCase ), _UpperCamelCase ) ) as in_file:
lowercase : List[Any] = in_file.read()
lowercase : str... | 337 |
from __future__ import annotations
def __lowercase ( _UpperCamelCase ) ->float:
"""simple docstring"""
if not nums:
raise ValueError('''List is empty''' )
return sum(_UpperCamelCase ) / len(_UpperCamelCase )
if __name__ == "__main__":
import doctest
docte... | 337 | 1 |
import re
import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey
from ..utils import logging
_snake_case = logging.get_logger(__name__)
def _A ( __magic_name__ ):
lowercase__ = R"\w+[.]\d+"
lowercase__ =... | 201 |
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments
import transformers
from transformers import (
AutoConfig,
AutoModelForSeqaSeqLM,
AutoTokeniz... | 201 | 1 |
'''simple docstring'''
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import numpy as np
import pandas as pd
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
BartForSequenc... | 27 |
"""simple docstring"""
import collections
import inspect
import unittest
from transformers import SwinvaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
... | 16 | 0 |
lowerCamelCase_ = """
# Transformers 설치 방법
! pip install transformers datasets
# 마지막 릴리스 대신 소스에서 설치하려면, 위 명령을 주석으로 바꾸고 아래 명령을 해제하세요.
# ! pip install git+https://github.com/huggingface/transformers.git
"""
lowerCamelCase_ = [{"""type""": """code""", """content""": INSTALL_CONTENT}]... | 14 |
from __future__ import annotations
from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass
@dataclass
class a_ :
'''simple docstring'''
__a: int
__a: int
class a_ :
... | 14 | 1 |
from __future__ import annotations
def A ( _SCREAMING_SNAKE_CASE ) -> bool:
lowerCamelCase : int = str(_SCREAMING_SNAKE_CASE )
return len(_SCREAMING_SNAKE_CASE ) == 9 and set(_SCREAMING_SNAKE_CASE ) == set("123456789" )
def A ( )... | 48 |
"""simple docstring"""
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device
if is_torch_available():
from transformers import PyTorchBenchmark, PyTorchBenchmarkArgu... | 266 | 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_... | 143 | 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 DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging
logging.... | 143 | 1 |
'''simple docstring'''
import argparse
import os
import torch
from transformers import FlavaConfig, FlavaForPreTraining
from transformers.models.flava.convert_dalle_to_flava_codebook import convert_dalle_checkpoint
def __snake_case ( UpperCAmelCase_ : Dict ):
# encoder.... | 55 |
'''simple docstring'''
import os
from itertools import chain
from random import randrange, shuffle
import pytest
from .sola import PokerHand
lowerCAmelCase :Tuple = (
'''4S 3H 2C 7S 5H''',
'''9D 8H 2C 6S 7H''',
'''2D 6D 9D TH 7D''',
'''TC 8C 2S JH 6C''',
'''JH 8S TH AH QH''',
'... | 331 | 0 |
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING:
import torch
class lowerCamel... | 361 | import argparse
import glob
import logging
import os
from argparse import Namespace
from importlib import import_module
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from to... | 105 | 0 |
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_ = logging.get_logger(__name__)
UpperCAmelCase_ =... | 201 |
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_ = logging.get_logger(__name__)
UpperCAmelCase_ =... | 201 | 1 |
from functools import reduce
SCREAMING_SNAKE_CASE__ = (
'73167176531330624919225119674426574742355349194934'
'96983520312774506326239578318016984801869478851843'
'85861560789112949495459501737958331952853208805511'
'12540698747158523863050715693290963295227443043557'
'668966489504452445... | 359 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applic... | 297 | 0 |
_lowerCamelCase : Tuple = """
# Transformers 설치 방법
! pip install transformers datasets
# 마지막 릴리스 대신 소스에서 설치하려면, 위 명령을 주석으로 바꾸고 아래 명령을 해제하세요.
# ! pip install git+https://github.com/huggingface/transformers.git
"""
_lowerCamelCase : Tuple = [{"""type""": """code""", """content""": IN... | 14 |
def SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ ) -> int:
"""simple docstring"""
return int(input_a == input_a == 0 )
def SCREAMING_SNAKE_CASE ( ) -> None:
"""simple docstring"""
print('''Truth Table of NOR Gate:''' )... | 14 | 1 |
from typing import List
from .keymap import KEYMAP, get_character
def UpperCamelCase__( UpperCamelCase__ : str )->Union[str, Any]:
def decorator(UpperCamelCase__ : Dict ):
A__ = getattr(_lowerCamelCase , '''handle_key''' , [] )
... | 369 |
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, Deco... | 39 | 0 |
from typing import Optional
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forw... | 143 | import argparse
import json
import os
import re
import torch
from transformers import BloomConfig, BloomModel
from transformers.file_utils import CONFIG_NAME, WEIGHTS_NAME
from transformers.utils import logging
logging.set_verbosity_info()
lowerCAmelCase__ : int = [
'''word_embeddings_l... | 143 | 1 |
'''simple docstring'''
import random
import unittest
import torch
from diffusers import IFInpaintingSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device... | 353 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_lowercase : Optional[Any] = logging.get_logger(__nam... | 264 | 0 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__a = {
'configuration_informer': [
'INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'InformerConfig',
],
}
try:
i... | 30 |
"""simple docstring"""
import unittest
from transformers import MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, AutoTokenizer, is_vision_available
from transformers.pipelines import pipeline
from transformers.pipelines.document_question_answering import apply_tesseract
from transformers.testin... | 105 | 0 |
import enum
import warnings
from .. import MODEL_FOR_CAUSAL_LM_MAPPING, TF_MODEL_FOR_CAUSAL_LM_MAPPING
from ..utils import add_end_docstrings, is_tf_available
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
class A (enum.Enum ):
... | 276 |
import argparse
import torch
from transformers import BertForMaskedLM
if __name__ == "__main__":
A : List[str] = argparse.ArgumentParser(
description=(
'''Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned'''
... | 276 | 1 |
"""simple docstring"""
from ....configuration_utils import PretrainedConfig
from ....utils import logging
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
# TODO: upload to AWS
SCREAMING_SNAKE_CASE__ = {
"yjernite/retribert-base-uncased": (
"https://huggingface.co/yjernite/ret... | 46 |
'''simple docstring'''
def lowerCamelCase__ ( _A , _A ):
while second != 0:
a : Union[str, Any] = first & second
first ^= second
a : Tuple = c << 1
return first
if __name__ == "__main__":
import doctest
doctest.testmod(... | 297 | 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... | 337 |
'''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,
TrainerCa... | 337 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase_ : Any = logging.get_logger(__name__)
lowerCAmelCase_ : Any = ... | 63 |
def __A ( __lowerCAmelCase )-> list:
"""simple docstring"""
if len(__lowerCAmelCase ) < 2:
return collection
def circle_sort_util(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> bool:
_UpperCAmelCase = False
... | 39 | 0 |
import numpy as np
import datasets
__UpperCAmelCase = '\nCompute the Mahalanobis Distance\n\nMahalonobis distance is the distance between a point and a distribution.\nAnd not between two distinct points. It is effectively a multivariate equivalent of the Euclidean distance.\nIt was... | 145 |
def lowercase__ ( __snake_case : List[str] , __snake_case : List[str] , __snake_case : Union[str, Any] , __snake_case : Optional[int] , __snake_case : str , __snake_case : Optional[Any] ):
'''simple docstring'''
... | 145 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ : List[Any] = logging.get_logger(__name__)
lowercase__ : Dict = {'''ctrl''': '''https://huggingface.co/ctrl/resolve/main/config.json'''}
class lowercase_ ( lowerCAmelCase__ ):
"""... | 338 |
"""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 import i... | 264 | 0 |
'''simple docstring'''
# 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.... | 72 |
'''simple docstring'''
from collections import OrderedDict
from typing import List, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a : List[str] = logging.get_logger(__name__)
a : Tuple ... | 72 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_torch_available,
)
A__: Any = {
'''configuration_speecht5''': [
'''SPEECHT5_PRETRAIN... | 276 |
'''simple docstring'''
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_sq... | 276 | 1 |
import string
import numpy
def a__ ( UpperCAmelCase : int , UpperCAmelCase : int ) -> int:
return b if a == 0 else greatest_common_divisor(b % a , UpperCAmelCase )
class __UpperCAmelCase :
UpperCamelCase = string.ascii_uppercas... | 99 |
import inspect
import unittest
from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_configuration_common imp... | 99 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__a = {
'''configuration_rembert''': ['''REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', ... | 337 |
import argparse
import pytorch_lightning as pl
import torch
from torch import nn
from transformers import LongformerForQuestionAnswering, LongformerModel
class __SCREAMING_SNAKE_CASE ( pl.LightningModule ):
def __init__( self , SCREAMING_SNAKE_CASE__ ):
super... | 337 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_A = {
'configuration_time_series_transformer': [
'TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'TimeSeriesTransformerConfig',
],
}
try:
if not is_tor... | 368 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bert import BertTokenizer
_A = logging.get_logger(__name__)
_A = {'vocab_file': 'vocab.txt', 'token... | 117 | 0 |
'''simple docstring'''
import argparse
import struct
import unittest
class A__ :
"""simple docstring"""
def __init__( self : Tuple , lowerCAmelCase__ : bytes ) -> None:
"""simple docstring"""
_UpperCAmelCase : Tuple = ... | 145 | '''simple docstring'''
import warnings
from ..trainer import Trainer
from ..utils import logging
__a = logging.get_logger(__name__)
class A__ ( UpperCamelCase ):
"""simple docstring"""
def __init__( self : Optional[int] , lowerCAmelCase__ : ... | 145 | 1 |
from __future__ import annotations
import time
import numpy as np
__snake_case : Optional[int] =[8, 5, 9, 7]
__snake_case : Optional[Any] =[
[2, 0, 1, 1],
[0, 1, 2, 1],
[4, 0, 0, 3],
[0, 2, 1, 0],
[1, 0, 3, 0],
]
__snake_case : int =[
[3, 2, 1, 4],
[0, 2, 5, 2],
[5,... | 367 |
def lowerCAmelCase__ ( lowerCamelCase_ : int = 1000):
'''simple docstring'''
lowerCAmelCase__ , lowerCAmelCase__ : int = 1, 1
lowerCAmelCase__ : Any = 2
while True:
lowerCAmelCase__ : Optional[Any] = 0
... | 94 | 0 |
"""simple docstring"""
import argparse
import os
import transformers
from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging
logging.set_verbosity_info()
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {name: getattr(transformers, na... | 72 |
"""simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
'''microsoft/unispeech-large-1500h-cv''': (
'''https://huggingface.c... | 72 | 1 |
"""simple docstring"""
from __future__ import annotations
def __lowercase ( _a , _a ):
if nth_term == "":
return [""]
snake_case_ : List[str] = int(_a )
snake_case_ : str = int(_a )
snake_case_ : list[str] = []
for temp i... | 155 |
"""simple docstring"""
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoFeatureExtractor, WavaVecaFeatureExtractor
from transformers.testing_uti... | 155 | 1 |
def A_ ( A__ , A__ , A__ ) -> float:
if principal <= 0:
raise Exception('Principal borrowed must be > 0' )
if rate_per_annum < 0:
raise Exception('Rate of interest must be >= 0' )
if years_to_repay <= 0 or not isinstance(A__ , A__ )... | 99 |
import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize
def A_ ( A__ ) -> float:
return np.dot(A__ , A__ )
class A__ :
"""simple docstring"""
def __init__( self , *,
lowercase = np.inf , lowercas... | 99 | 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 Tokeni... | 352 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCamelCase__ : List[Any] = logging.get_logger(... | 164 | 0 |
'''simple docstring'''
from functools import lru_cache
@lru_cache
def _A ( A__ ):
"""simple docstring"""
if num < 0:
raise ValueError('''Number should not be negative.''' )
return 1 if num in (0, 1) else num * factorial(num - 1 )
if __name__ == "__main__":
import doct... | 104 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bert import BertTokenizer
snake_case__ : List[Any] = logging.get_logg... | 117 | 0 |
"""simple docstring"""
def UpperCamelCase_ ( lowerCAmelCase__ : str ) -> bool:
"""simple docstring"""
return credit_card_number.startswith(('34', '35', '37', '4', '5', '6') )
def UpperCamelCase_ ( lowerCAmelCase__ : str ) ... | 289 |
"""simple docstring"""
import os
from bleurt import score # From: git+https://github.com/google-research/bleurt.git
import datasets
lowercase__ : Tuple = datasets.logging.get_logger(__name__)
lowercase__ : List[Any] = """\
@inproceedings{bleurt,
titl... | 289 | 1 |
'''simple docstring'''
import os
import socket
from contextlib import contextmanager
import torch
from ..commands.config.default import write_basic_config # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
fro... | 297 |
def __lowerCamelCase ( UpperCAmelCase_ : list , UpperCAmelCase_ : list , UpperCAmelCase_ : int ):
"""simple docstring"""
if len(UpperCAmelCase_ ) != len(UpperCAmelCase_ ):
raise ValueError('''The length of profit and weight must be same.'''... | 94 | 0 |
from math import cos, sin, sqrt, tau
from audio_filters.iir_filter import IIRFilter
def UpperCamelCase ( _A, _A, _A = 1 / sqrt(2 ) ):
"""simple docstring"""
__magic_name__ : Union[str, Any] = tau * frequency / samplerate
__magic_name__ ... | 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"""
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 transformers ... | 155 |
"""simple docstring"""
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, Bert... | 155 | 1 |
import jax.numpy as jnp
from ...utils import logging
from ..ta.modeling_flax_ta import FlaxTaEncoderModel, FlaxTaForConditionalGeneration, FlaxTaModel
from .configuration_mta import MTaConfig
lowerCAmelCase__ : Tuple =logging.get_logger(__name__)
lowerCAmelCase__ : Union[str, Any] ... | 162 |
import os
import socket
from contextlib import contextmanager
import torch
from ..commands.config.default import write_basic_config # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
from .transformer_en... | 162 | 1 |
'''simple docstring'''
from __future__ import annotations
import collections
import pprint
from pathlib import Path
def _A ( lowercase__ ):
return "".join(sorted(lowercase__ ) )
def _A ( lowercase__ ):
return word_by_signature[signature(lowercase__ )]
... | 164 |
'''simple docstring'''
import os
import sys
__A = os.path.join(os.path.dirname(__file__), "src")
sys.path.append(SRC_DIR)
from transformers import (
AutoConfig,
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForQuestionAnswering,
AutoModelForSequenceClassificatio... | 164 | 1 |
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 __snake_case ( _UpperCAmelCase ... | 131 |
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion_safe... | 131 | 1 |
"""simple docstring"""
import heapq
import sys
import numpy as np
UpperCAmelCase__ = tuple[int, int]
class a :
def __init__( self : Any ):
_UpperCAmelCase = []
_UpperCAmelCase = set()
def lowerCAmelCase_ ( self : List[str] ... | 289 | """simple docstring"""
import gc
import unittest
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
PriorTransformer,
StableUnCLIPPipeline,
UNetaDConditionModel,
... | 289 | 1 |
'''simple docstring'''
# Usage:
# ./gen-card-facebook-wmt19.py
import os
from pathlib import Path
def SCREAMING_SNAKE_CASE( __lowercase , __lowercase , __lowercase ) -> Any:
A: Dict = {
'''en''': '''Machine learning is great, isn\... | 334 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_tf_available,
is_torch_available,
)
UpperCamelCase = {
'''configuration_speec... | 334 | 1 |
def __UpperCamelCase ( _lowerCAmelCase ) -> "list[int]":
"""simple docstring"""
if upper_limit < 0:
raise ValueError("""Limit for the Catalan sequence must be ≥ 0""" )
A : Dict = [0] * (upper_limit + 1)
# Base case: C(0) = C(1) = 1
A : List[An... | 116 |
__A : dict[tuple[int, int, int], int] = {}
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase ) -> int:
'''simple docstring'''
if late == 3 or absent == 2:
return 0
# if we have no days left, and have not failed any... | 138 | 0 |
from argparse import ArgumentParser
from . import BaseTransformersCLICommand
def lowerCamelCase__ ( a__ : str ) -> List[str]:
return DownloadCommand(args.model , args.cache_dir , args.force , args.trust_remote_code )
class lowercase_ ( __SCREAMING_SNAKE... | 261 |
def lowerCamelCase__ ( a__ : Optional[int] , a__ : Any ) -> Optional[Any]:
UpperCamelCase_ = 0
UpperCamelCase_ = len(a__ ) - 1
while left <= right:
# avoid divided by 0 during interpolation
if sorted_co... | 261 | 1 |
'''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 UpperCAmelCase__ ( UpperCAmelCase__ ) -> Dict:
A_... | 162 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__lowerCamelCase = {
'''configuration_clap''': [
'''CLAP_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''ClapAudioConfig''',
'''ClapConfig'... | 162 | 1 |
"""simple docstring"""
import heapq
import sys
import numpy as np
a :Union[str, Any] = tuple[int, int]
class __a :
'''simple docstring'''
def __init__( self ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = []
... | 355 |
"""simple docstring"""
import math
from collections.abc import Iterator
from itertools import takewhile
def _lowercase ( __lowerCAmelCase ) -> bool:
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
... | 56 | 0 |
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 TokenizerTesterMixin
@require_t... | 131 |
import unittest
from transformers import DonutProcessor
lowerCamelCase = '''naver-clova-ix/donut-base'''
class _a ( unittest.TestCase):
def UpperCAmelCase__( self : str )-> int:
lowerCAmelCase__ : Any = DonutProcessor.fro... | 131 | 1 |
from math import factorial, radians
def UpperCAmelCase_ (_lowerCAmelCase : float , _lowerCAmelCase : int = 18 , _lowerCAmelCase : int = 10 ):
__UpperCamelCase : Any = angle_in_degrees - ((angle_in_degrees // 360.0) * 360.0)
# Converting from ... | 171 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowercase : Union[str, Any] = {"configuration_glpn": ["GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP", "GLPNConfig"]}
try:
if not is_vision_available():
r... | 171 | 1 |
# Usage:
# ./gen-card-facebook-wmt19.py
import os
from pathlib import Path
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE ={
'en': 'Machine learning is great, isn\'t it?',
'r... | 334 |
import os
from typing import List, Optional, Union
from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
from ...utils import logging
_lowerCamelCase =logging.get_logger(__name__)
_lowerCamelCase ={"vocab_file": "vocab.txt"}
_lowerCamelC... | 334 | 1 |
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import TimesformerConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils impor... | 173 |
import json
import os
import tempfile
import transformers
import datasets
from utils import generate_example_dataset, get_duration
__a = 50_00_00
__a , __a = os.path.split(__file__)
__a = os.path.join(RESULTS_BASEPATH, '''results''', RESULTS_FILENAME.replace('''.py''', '''.json'''))
@get... | 173 | 1 |
"""simple docstring"""
def _lowerCamelCase( a ):
if length <= 0 or not isinstance(a , a ):
raise ValueError("Length must be a positive integer." )
return [n * (2 * n - 1) for n in range(a )]
if __name__ == "__main__":
print(hexagonal_numbers(len... | 261 | """simple docstring"""
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
SCREAMING_SNAKE_CASE__:List[str] = {"""configuration_van""": ["""VAN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """VanConfig"""]}
try:
if not... | 261 | 1 |
import argparse
import shutil
import time
from json import JSONDecodeError
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import (... | 200 |
import unittest
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
if is_torch_available():
import torch
from transformers import AutoModelForImageClassification
if is_vision_available():
from tr... | 200 | 1 |
import itertools
import random
import unittest
import numpy as np
from transformers import WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST, WavaVecaConfig, WavaVecaFeatureExtractor
from transformers.testing_utils import require_torch, slow
from ...test_sequence_feature_extraction_common import SequenceFeatureExtracti... | 26 |
'''simple docstring'''
from collections import defaultdict
def __magic_name__ ( __UpperCAmelCase ) -> int:
'''simple docstring'''
snake_case_ = 1
snake_case_ = True
for v in tree[start]:
if v not in visited:
ret += dfs(__UpperCAmelCa... | 56 | 0 |
"""simple docstring"""
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDe... | 230 |
"""simple docstring"""
from typing import Optional
from torch import nn
from .transformer_ad import TransformeraDModel, TransformeraDModelOutput
class UpperCAmelCase_ ( nn.Module ):
def __init__( self : Optional[int] , snake_case_ : int = 16 , snake_case_ ... | 230 | 1 |
"""simple docstring"""
from numpy import exp, pi, sqrt
def a__ ( lowerCAmelCase , lowerCAmelCase = 0.0 , lowerCAmelCase = 1.0 ) -> int:
return 1 / sqrt(2 * pi * sigma**2 ) * exp(-((x - mu) ** 2) / (2 * sigma**2) )
if __name__ == "__main__":
import doctest
doctes... | 171 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
... | 171 | 1 |
'''simple docstring'''
import math
import sys
def UpperCamelCase_ ( A__ : str ):
'''simple docstring'''
lowerCAmelCase_ : List[Any] = """"""
try:
with open(A__ , """rb""" ) as binary_file:
lowerCAmelCase_ ... | 89 |
'''simple docstring'''
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licen... | 89 | 1 |
"""simple docstring"""
import argparse
import torch
from torch import nn
from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def __magic_name__ ( lowercase ):
SCREAMING_SNAKE_CASE_: int =[
"""encoder.version""",
"""decoder.version"... | 173 |
"""simple docstring"""
import datasets
from .evaluate import evaluate
_UpperCAmelCase = """\
@inproceedings{Rajpurkar2016SQuAD10,
title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},
author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},
booktitle={EMN... | 173 | 1 |
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,
)
SCREAMING_SNAKE_CASE : Any = {
... | 354 |
import argparse
import gc
import json
import os
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 im... | 252 | 0 |
'''simple docstring'''
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ : Optional[int] = logging.get_logger(__name__)
UpperCAmelCase_ : Dict = {
'facebook/wav2vec2-base-960h': 'https://huggingface.co... | 200 |
'''simple docstring'''
def snake_case_ ( SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE : int = 0
while len(SCREAMING_SNAKE_CASE__ ) > 1:
_SCREAMING_SNAKE_CASE : Any = 0
# Consider two files with minimum... | 200 | 1 |
"""simple docstring"""
def lowerCAmelCase_ (lowerCAmelCase__: int = 5_0 ):
"""simple docstring"""
UpperCAmelCase_: Optional[Any] = [1] * (length + 1)
for row_length in range(3 , length + 1 ):
for block_length in range(3... | 366 |
import argparse
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
a : List[... | 82 | 0 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.activations import gelu_new, gelu_python, get_activation
@require_torch
class a ( unittest.TestCase ... | 230 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.activations import gelu_new, gelu_python, get_activation
@require_torch
class a ( unittest.TestCase ... | 230 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case_ : str = logging.get_logger(__name__)
snake_case_ : List[Any] = {
"google/realm-cc-news-pretrained-embedder": (
"https://huggingface.co/google/realm-cc-news-pretrained-embedder/re... | 7 |
import inspect
import os
import re
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstring... | 7 | 1 |
'''simple docstring'''
from datetime import datetime as dt
import os
from github import Github
__lowerCAmelCase = [
'''good first issue''',
'''good second issue''',
'''good difficult issue''',
'''feature request''',
'''new model''',
'''wip''',
]
def __lowerCamelCase ... | 89 |
'''simple docstring'''
import argparse
import json
import torch
from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel
def __lowerCamelCase ( lowerCAmelCase_ , lowerCAmelCase_=1 ) -> Dict:
if n_shave_prefix_segments >= 0:
return ".".join(path.split('.' )[n_s... | 89 | 1 |
"""simple docstring"""
import collections
import gzip
import os
import urllib
import numpy
from tensorflow.python.framework import dtypes, random_seed
from tensorflow.python.platform import gfile
from tensorflow.python.util.deprecation import deprecated
UpperCamelCase_ = collections.namedtuple('_Datasets', [... | 303 |
"""simple docstring"""
import math
UpperCamelCase_ = 10
UpperCamelCase_ = 7
UpperCamelCase_ = BALLS_PER_COLOUR * NUM_COLOURS
def UpperCamelCase ( UpperCAmelCase = 20 ) ->str:
"""simple docstring"""
a_ = math.comb(UpperCAmelCase , UpperCAmelCase )
a_ = math.comb... | 303 | 1 |
"""simple docstring"""
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import evaluate
import numpy as np
import torch
from datasets import load_dataset
from PIL import Image
from torchvision.transforms import (
CenterCrop,
Compose,
Normalize... | 86 |
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType,... | 252 | 0 |
import argparse
import os
import shutil
from pathlib import Path
import onnx
import torch
from packaging import version
from torch.onnx import export
from diffusers import OnnxRuntimeModel, OnnxStableDiffusionPipeline, StableDiffusionPipeline
A : Optional[int] = version.parse(version.pa... | 146 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A : Union[str, Any] = logging.get_logger(__name__)
A : int = {
'andreasmadsen/efficient_mlm_m0.40': ... | 146 | 1 |
from ...processing_utils import ProcessorMixin
class UpperCAmelCase_ ( a):
lowerCamelCase__ = 'SpeechT5FeatureExtractor'
lowerCamelCase__ = 'SpeechT5Tokenizer'
def __init__( self, __a, __a):
'''simple docstring'''
super().__in... | 36 |
import argparse
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
A__ = logging.get_logger... | 82 | 0 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
f... | 354 |
"""simple docstring"""
import argparse
import os
import torch
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
a = {
'''sample_size''': 32,
'''in_channels''': 3,
'''out_channels''': 3,
'''layers_per_block''': 2,
'''nu... | 271 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"google/realm-cc-news-pretrained-embedder": (
"https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/config.json"
... | 7 |
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_output_indices
... | 7 | 1 |
def __a ( UpperCAmelCase = 1000 ) ->int:
"""simple docstring"""
A = 2**power
A = 0
while n:
A , A = r + n % 10, n // 10
return r
if __name__ == "__main__":
print(solution(int(str(input()).strip())))
| 355 |
'''simple docstring'''
from __future__ import annotations
def __a ( UpperCAmelCase ) ->list[int]:
"""simple docstring"""
return [ord(UpperCAmelCase ) - 96 for elem in plain]
def __a ( UpperCAmelCase ) ->str:
"""simple docstring"""
return "".join... | 337 | 0 |
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 transformers import TFCamembertModel
@r... | 303 |
from heapq import heappop, heappush
import numpy as np
def a__ ( snake_case , snake_case , snake_case , snake_case , ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE, __SCREAMING_SNAKE_CASE : int = grid.shape
__SCREAMING_SNAKE_CASE : Tuple = ... | 303 | 1 |
import collections
import os
from typing import List, Optional, Tuple
from transformers.utils import is_jieba_available, requires_backends
if is_jieba_available():
import jieba
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
snake_case_ : Optional... | 7 |
import inspect
import os
import re
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstring... | 7 | 1 |
def _a ( SCREAMING_SNAKE_CASE : int = 1000 ):
"""simple docstring"""
UpperCamelCase__ : Optional[int] = 3
UpperCamelCase__ : List[str] = 0
while a < n:
if a % 3 == 0 or a % 5 == 0:
result += a
elif a % 15 == 0:
result -= a
a += 1
return result
... | 146 |
import cmath
import math
def _a ( SCREAMING_SNAKE_CASE : float , SCREAMING_SNAKE_CASE : float , SCREAMING_SNAKE_CASE : float , SCREAMING_SNAKE_CASE : float ):
"""simple docstring"""
UpperCamelCase__ : Union[str, Any] = math.radians(SCREAMING_SNAKE_CA... | 146 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_snake_case = {
'''configuration_distilbert''': [
'''DISTILBERT_PRETRAINED_... | 362 | import json
import os
import shutil
import tempfile
import unittest
from transformers import BatchEncoding, CanineTokenizer
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.tokenization_utils import AddedToken
from transformers.utils import cached_property
from ...... | 342 | 0 |
"""simple docstring"""
import argparse
import json
from typing import List
from ltp import LTP
from transformers import BertTokenizer
def lowercase ( _snake_case : List[Any] ) ->Tuple:
"""simple docstring"""
if (
(cp >= 0x4e00 and cp <= 0x9fff)
or (cp >= 0x3400 ... | 102 |
'''simple docstring'''
def UpperCAmelCase_ (__a : list , __a : list , __a : int ):
"""simple docstring"""
_a : Optional[Any] = len(__a )
_a : int = [[0] * n for i in range(__a )]
for i in range(__a ):
_a : Tuple = y_points[i]
for i ... | 271 | 0 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ConvNextConfig, SegformerImageProcessor, UperNetConfig, UperNetForSemanticSegmentation
def UpperCamelCase ( __lowercase : Any ) -> ... | 370 | import math
def UpperCamelCase ( __lowercase : int = 1_00 ):
'''simple docstring'''
A_ : List[Any] = sum(i * i for i in range(1 ,n + 1 ) )
A_ : int = int(math.pow(sum(range(1 ,n + 1 ) ) ,2 ) )
return sq... | 192 | 0 |
A_ :Union[str, Any] = {
0: '''0''',
1: '''1''',
2: '''2''',
3: '''3''',
4: '''4''',
5: '''5''',
6: '''6''',
7: '''7''',
8: '''8''',
9: '''9''',
10: '''a''',
11: '''b''',
12: '''c''',
13: '''d''',
14: '''e''',
... | 71 |
import os
import re
import shutil
import sys
import tempfile
import unittest
import black
__a = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, '''utils'''))
import check_copies # noqa: E402
# This is the reference code that wi... | 337 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
__snake_case : Optional[int] = {
"""configuration_mega""": ["""MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MegaConfig""", """MegaOnnxConfig"""],
... | 122 |
# 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 ... | 122 | 1 |
import collections
import os
from typing import List, Optional, Tuple
from transformers.utils import is_jieba_available, requires_backends
if is_jieba_available():
import jieba
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowercase_ = logging.g... | 7 |
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_output_indices
... | 7 | 1 |
import copy
from collections import OrderedDict
from typing import Dict, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
lowerCamelCase__ : str = logging... | 210 |
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class lowerCamelCase_ ( _SCREAMING_S... | 210 | 1 |
'''simple docstring'''
import math
def snake_case_ (_a : float , _a : float ):
return math.pow(_a , 2 ) - a
def snake_case_ (_a : float ):
return 2 * x
def snake_case_ (_a : float ):
UpperCAmelCa... | 34 |
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow
@require_torch
class snake_case__ ( unittest.TestCase ):
def __magic_name__ ( self ) ... | 342 | 0 |
import os
import tempfile
import unittest
from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property
@unittest.skipUnless(os.path.exists(UpperCamelCase__ ) , """Tato... | 180 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE ( __UpperCamelCase) -> list[int]:
a = 2
a = []
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.append(__UpperCamelCase)
if n... | 180 | 1 |
'''simple docstring'''
import argparse
import fairseq
import torch
from torch import nn
from transformers import (
MBartaaTokenizer,
MBartConfig,
MBartForCausalLM,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaModel,
... | 206 |
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_roformer import RoFormerTokenizer
from .tokeniza... | 192 | 0 |
"""simple docstring"""
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCamelCase : Optional[int] = logging.get_logger(__name__)
__UpperCamelCase : Dict = {
'''huggingface/informer-tourism-monthl... | 74 |
"""simple docstring"""
from __future__ import annotations
from math import pow, sqrt
def __SCREAMING_SNAKE_CASE ( A_ , A_ , A_ ):
if (resistance, reactance, impedance).count(0 ) != 1:
raise ValueError('''One and only one argument must be 0''' )
if resistance == 0:
return {"r... | 74 | 1 |
import argparse
import os
import sys
from unittest.mock import patch
import pytorch_lightning as pl
import timeout_decorator
import torch
from distillation import SummarizationDistiller, distill_main
from finetune import SummarizationModule, main
from transformers import MarianMTModel
from transformers.file_utils im... | 122 |
import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type
from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterators import ThreadedIterator
from tqdm import... | 122 | 1 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__lowerCamelCase : Dict = logging.get_logger(__name__)
__lowerCamelCase : Tuple = {"... | 286 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__lowerCamelCase : Any = {
"""configuration_instructblip""": [
"""INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""InstructBlipConfig""",
"""Instr... | 286 | 1 |
import argparse
from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging
logging.set_verbosity_info()
def UpperCAmelCase ( lowercase , lowercase , lowercase ):
"""simple docstring"""
__lowercas... | 210 | import argparse
import gc
import json
import os
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 Accelerator,... | 210 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
__snake_case = {
'''configuration_swiftformer''': [
'''SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''SwiftFormerConfig'... | 219 |
'''simple docstring'''
from math import ceil
def a ( __a , __a ) -> Any:
'''simple docstring'''
UpperCamelCase__ :str = list(range(0 , __a ) )
UpperCamelCase__ :Optional[int] = [item for sublist in list(device_map.value... | 219 | 1 |
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