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'''
from __future__ import annotations
from math import ceil, floor, sqrt
def lowerCAmelCase (__A = 2_000_000):
"""simple docstring"""
_a = [0]
_a = 42
for idx in range(1 , ceil(sqrt(target * 2) * 1.1)):
triangle_numbers.append(triangle... | 211 |
'''simple docstring'''
import random
def lowerCAmelCase (__A):
"""simple docstring"""
_a = num - 1
_a = 0
while s % 2 == 0:
_a = s // 2
t += 1
for _ in range(5):
_a = random.randrange(2 , num - 1)
_a = ... | 211 | 1 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_yolos import YolosImageProcessor
lowerCamelCase__ = logging.get_logger(__name__)
class lowerCAmelCase__ ( UpperCAmelCase__ ):
def __init__( self : ... | 322 |
'''simple docstring'''
def __lowerCAmelCase (__lowerCAmelCase = 4_000_000 ):
_UpperCAmelCase : List[Any] = []
_UpperCAmelCase , _UpperCAmelCase : Dict = 0, 1
while b <= n:
if b % 2 == 0:
even_fibs.append(__lowerCAmelCase )
_UpperCAmelCase ... | 322 | 1 |
"""simple docstring"""
import functools
from typing import Any
def lowercase ( _snake_case : str , _snake_case : list[str] ) ->bool:
"""simple docstring"""
if not isinstance(_snake_case , _snake_case ) or len(_snake_case ) == 0:
raise ValueEr... | 102 | 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.set_verbosity_... | 118 | 0 |
import argparse
import json
import os
import fairseq
import torch
from torch import nn
from transformers import (
SpeechaTextaConfig,
SpeechaTextaForCausalLM,
SpeechaTextaTokenizer,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFeatureExtractor,
Wa... | 159 |
import warnings
from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor
_lowerCamelCase : List[str] = logging.get_logger(__name__)
class __snake_case (_a ):
def __init__( self : Optional[Any] , *_UpperCAmelCase : str , **_UpperCAmelCase... | 159 | 1 |
import os
from argparse import ArgumentParser, Namespace
from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand
if not is_tf_available() and no... | 68 |
"""simple docstring"""
import argparse
import torch
from transformers import LxmertConfig, LxmertForPreTraining, load_tf_weights_in_lxmert
from transformers.utils import logging
logging.set_verbosity_info()
def snake_case_ ( A_ : Tuple, A_ : int, A_ : Dict ):
... | 72 | 0 |
"""simple docstring"""
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 ... | 359 |
"""simple docstring"""
import random
import timeit
from functools import wraps
from typing import Callable, Optional
from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_tf_auto import TF_MODEL_MAPPING, TF_MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_tf_avail... | 157 | 0 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
... | 77 | """simple docstring"""
import argparse
import os
import shutil
import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
def a_ ( _lowerCAmelCase : str ):
'''simple docstring'''
lowercase__ : int = args.pruning_method
... | 77 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
_lowerCamelCase : List[str] = {
'configuration_efficientformer': [
'EFFICIENTFO... | 337 |
'''simple docstring'''
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
_lowerCamelCase : Dict = logging.get_logger(__name__)
_lowerCamelCase : Any = {
'google/umt5-small'... | 337 | 1 |
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
... | 322 |
def _a ( SCREAMING_SNAKE_CASE : int ) -> bool:
"""simple docstring"""
if not isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
__lowerCAmelCase: List[Any] = f'''Input value of [number={number}] must be an integer'''
raise TypeError(SCREAMING_SNAKE_CA... | 322 | 1 |
from __future__ import annotations
def _a ( lowerCamelCase: list[float] ) -> float:
'''simple docstring'''
__A = 0.00
__A = 0
for resistor in resistors:
if resistor <= 0:
... | 250 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case__ : str = logging.get_logger(__name__)
snake_case__ : Optional[int] = {
'google/pix2struct-textcaps-base':... | 250 | 1 |
from collections import deque
class __lowerCAmelCase :
"""simple docstring"""
def __init__( self : Dict , _lowerCAmelCase : str , _lowerCAmelCase : int , _lowerCAmelCase : int ) -> None:
"""simple docstring"""
... | 159 |
import datasets
import faiss
import numpy as np
import streamlit as st
import torch
from elasticsearch import Elasticsearch
from elia_utils import (
embed_questions_for_retrieval,
make_qa_sas_model,
qa_sas_generate,
query_es_index,
query_qa_dense_index,
)
import transformers
fr... | 159 | 1 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
"microsoft/unispeech-large-1500h-cv": (
"https://huggingface.co/microsoft/unispeech-large-1500h-cv/re... | 362 |
from collections import namedtuple
import requests
from lxml import html # type: ignore
_snake_case = namedtuple("""covid_data""", """cases deaths recovered""")
def _A ( __magic_name__ = "https://www.worldometers.info/coronavirus/" ):
lowercase__ = "//div[@class = \"maincount... | 201 | 0 |
def SCREAMING_SNAKE_CASE_ ( __A : int ) -> int:
"""simple docstring"""
if not isinstance(snake_case__ , snake_case__ ):
raise ValueError('Input must be an integer' )
if input_num <= 0:
raise ValueError('Input must be positive'... | 32 | # Copyright 2021 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... | 157 | 0 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
... | 357 |
'''simple docstring'''
from .glue import GlueDataset, GlueDataTrainingArguments
from .language_modeling import (
LineByLineTextDataset,
LineByLineWithRefDataset,
LineByLineWithSOPTextDataset,
TextDataset,
TextDatasetForNextSentencePrediction,
)
from .squad import Squ... | 13 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
__a = {
'''configuration_efficientformer''': [
'''EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''... | 337 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
__a = logging.get_logger(__name__)
__a = {
'''ut/deta''': '''https://huggingface.co/ut/deta/resolve/main/config.json''',
}
class __SCREAMING_SNAKE_CASE ... | 337 | 1 |
import os
import unittest
from transformers.models.phobert.tokenization_phobert import VOCAB_FILES_NAMES, PhobertTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class A_ ( _lowerCamelCase , unittest.TestCase ):
lowerCAmelCase__ = PhobertToken... | 363 |
import math
def _a ( lowerCamelCase: int ) -> int:
'''simple docstring'''
if not isinstance(lowerCamelCase , lowerCamelCase ):
__A = F"""Input value of [number={number}] must be an integer"""
rais... | 250 | 0 |
'''simple docstring'''
def _A ( snake_case , snake_case ) -> str:
_lowercase : int = ""
for word_or_phrase in separated:
if not isinstance(snake_case , snake_case ):
raise Exception("join() accepts only strings to be joined" )
joined += word... | 250 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import RobertaPreLayerNormConfig, 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_avai... | 250 | 1 |
"""simple docstring"""
from .integrations import (
is_optuna_available,
is_ray_available,
is_sigopt_available,
is_wandb_available,
run_hp_search_optuna,
run_hp_search_ray,
run_hp_search_sigopt,
run_hp_search_wandb,
)
from .trainer_utils import (
HPSearchBackend,
default_hp_... | 357 | """simple docstring"""
import os
import re
import shutil
from argparse import ArgumentParser, Namespace
from datasets.commands import BaseDatasetsCLICommand
from datasets.utils.logging import get_logger
__A = "<<<<<<< This should probably be modified because it mentions: "
__A = "=... | 2 | 0 |
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 A__ ( unittest.TestCase ):
def __UpperCamelCase( self ):
'... | 52 |
import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
def ... | 201 | 0 |
'''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 i... | 358 |
'''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_au... | 311 | 0 |
from pathlib import Path
import numpy as np
from PIL import Image
def lowercase_( SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
lowerCamelCase : Optional[Any] = rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
return 0.2989 * r + 0.5870 * g + 0.1140 * b
def lo... | 283 |
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XGLMTokenizer, XGLMTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_tokenization_commo... | 13 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__a = {
'configuration_convbert': ['CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ConvBertConf... | 370 | '''simple docstring'''
import os
import pytest
import yaml
from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
"files", [
["full:README.md", "dataset_infos.json"],
["empty:README.md", "dataset_infos.js... | 17 | 0 |
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__ = {
"xlm-mlm-en-2048": "https://h... | 0 |
'''simple docstring'''
import argparse
import torch
from transformers import (
SpeechTaConfig,
SpeechTaFeatureExtractor,
SpeechTaForSpeechToSpeech,
SpeechTaForSpeechToText,
SpeechTaForTextToSpeech,
SpeechTaProcessor,
SpeechTaTokenizer,
logging,
)
from transformers.tokenization_utils ... | 250 | 0 |
"""simple docstring"""
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Image
from .base import TaskTemplate
@dataclass(frozen=_SCREAMING_SNAKE_CASE )
class __A ( _SCREAMING_SNAKE_CASE... | 215 |
"""simple docstring"""
# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import flax
import jax
import jax.numpy as jnp
from ..configuration_utils import ConfigMixin, register_... | 215 | 1 |
class lowercase__:
"""simple docstring"""
def __init__( self : int , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : List[str]=None , SCREAMING_SNAKE_CASE_ : Union[str, Any]=None ) -> List[str]:
lowercase_ = ... | 30 |
'''simple docstring'''
import unittest
from transformers import TrOCRConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import Mo... | 2 | 0 |
"""simple docstring"""
import copy
import os
from typing import TYPE_CHECKING, List, Union
if TYPE_CHECKING:
pass
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : str = logging.get_logger(__name__)
a : int = ... | 350 |
"""simple docstring"""
def _SCREAMING_SNAKE_CASE ( _lowercase : int ) ->bool:
'''simple docstring'''
return number & 1 == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 79 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
lowerCamelCase : Union[str, Any] = {
"configuration_encodec": [
"ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP",
"EncodecConfig",
],
"f... | 124 |
'''simple docstring'''
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, AutoToken... | 311 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__lowercase = {
"configuration_lxmert": ["LXMERT_PRETRAINED_CONFIG... | 354 |
"""simple docstring"""
__lowercase = frozenset(
[
"""prompt""",
"""height""",
"""width""",
"""guidance_scale""",
"""negative_prompt""",
"""prompt_embeds""",
"""negative_prompt_embeds""",
"""cross_attention_kwargs""",... | 226 | 0 |
from collections import defaultdict
from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst
def lowerCAmelCase_ ( ) -> str:
'''simple docstring'''
UpperCAmelCase__ , UpperCAmelCase__ = 9, 14 # noqa: F841
UpperCAmelCase__ ... | 65 |
"""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.testing_utils import (
... | 17 | 0 |
'''simple docstring'''
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import BatchEncoding, MarianTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import is_sentencepiece_available, is_tf_a... | 48 |
'''simple docstring'''
from __future__ import annotations
import requests
def _A ( _lowerCAmelCase ):
"""simple docstring"""
__lowercase =f"""https://hacker-news.firebaseio.com/v0/item/{story_id}.json?print=pretty"""
return requests.get(_lowerCAmelCase ).json()
... | 48 | 1 |
'''simple docstring'''
from ...processing_utils import ProcessorMixin
class lowercase ( _lowerCamelCase ):
"""simple docstring"""
UpperCAmelCase = """WhisperFeatureExtractor"""
UpperCAmelCase = """WhisperTokenizer"""
def __init__( self ,a_ ,a_ ) ... | 215 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
A_ : Optional[Any] = {
"""configuration_llama""": ["""LLAMA_PRETRAINED_C... | 215 | 1 |
import unittest
from diffusers import FlaxAutoencoderKL
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax
from .test_modeling_common_flax import FlaxModelTesterMixin
if is_flax_available():
import jax
@require_flax
class _a... | 82 |
from typing import TYPE_CHECKING
from ....utils import _LazyModule
a : Tuple = {'tokenization_tapex': ['TapexTokenizer']}
if TYPE_CHECKING:
from .tokenization_tapex import TapexTokenizer
else:
import sys
a : List[Any] = _LazyModule(__name__, globals()['_... | 82 | 1 |
import random
from typing import Any
def _snake_case( SCREAMING_SNAKE_CASE__ ) -> list[Any]:
for _ in range(len(SCREAMING_SNAKE_CASE__ ) ):
lowercase : List[Any] = random.randint(0 , len(SCREAMING_SNAKE_CASE__ ) - 1 )
lowercas... | 20 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCamelCase_ = {
'''configuration_longformer''': [
'''LONGFORMER_PRE... | 79 | 0 |
"""simple docstring"""
from __future__ import annotations
import numpy as np
from numpy import floataa
from numpy.typing import NDArray
def _snake_case ( UpperCamelCase : Dict , UpperCamelCase : str , UpperCamelCase : Optional[Any] , UpperCamelCase : Any ... | 359 |
"""simple docstring"""
def _snake_case ( UpperCamelCase : list , UpperCamelCase : list ):
_validate_point(UpperCamelCase )
_validate_point(UpperCamelCase )
if len(UpperCamelCase ) != len(UpperCamelCase ):
raise ValueError("""Both points must be in the sa... | 76 | 0 |
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import Sta... | 87 |
from itertools import product
from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros
def a ( _UpperCAmelCase : Tuple , _UpperCAmelCase : Optional[int] ):
'''simple do... | 226 | 0 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModel,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import AutoencoderKL, DDIMScheduler, DD... | 54 | """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 UpperCAmelCase_ ( _lowercase):
... | 54 | 1 |
# Algorithm for the pigeonhole sorting
def A ( _SCREAMING_SNAKE_CASE ) -> List[Any]:
lowerCamelCase : Any = min(_SCREAMING_SNAKE_CASE ) # min() finds the minimum value
lowerCamelCase : Union[str, Any] = max(_SCREAMING_SNAKE_CASE ) # ... | 48 |
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
... | 48 | 1 |
import warnings
from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum
from ...processing_utils import ProcessorMixin
if is_torch_available():
import torch
class __snake_case ( lowerCamelCase_ ... | 204 | from __future__ import annotations
__lowerCamelCase : Dict = [-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0]
__lowerCamelCase : Union[str, Any] = [-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1]
def __SCREAMING_SNAKE_CASE ( __UpperCamelCas... | 204 | 1 |
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def _UpperCAmelCase ( ):
"""simple docstring"""
_lowerCAmelCase = ArgumentParser(
description=(
"""Py... | 82 |
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
_lowerCAmelCase = 1
for i in range(1 , num + 1 ):
fact *= i
return fact
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
_lowerCAmelCase = 0
... | 82 | 1 |
"""simple docstring"""
import argparse
import os
from pathlib import Path
import torch
from bark.generation import _load_model as _bark_load_model
from huggingface_hub import hf_hub_download
from transformers import EncodecConfig, EncodecModel, set_seed
from transformers.models.bark.configuration_bark import (... | 212 |
"""simple docstring"""
import os
from collections import deque
import torch
from torch.utils.data import Dataset
class _lowerCamelCase ( a_ ):
def __init__( self : Tuple , UpperCamelCase : List[Any]="" , UpperCamelCase : List[str]="train" ) -> Li... | 212 | 1 |
"""simple docstring"""
import pytest
from datasets.utils.sharding import _distribute_shards, _number_of_shards_in_gen_kwargs, _split_gen_kwargs
@pytest.mark.parametrize(
'kwargs, expected' , [
({'num_shards': 0, 'max_num_jobs': 1}, []),
({'num_sha... | 96 |
from math import factorial
def lowerCamelCase__ ( _a , _a , _a):
if successes > trials:
raise ValueError("successes must be lower or equal to trials")
if trials < 0 or successes < 0:
raise ValueError("the function is defined for non-negative integers")
if not isinstance(_a , ... | 76 | 0 |
import contextlib
import csv
import json
import os
import sqlitea
import tarfile
import textwrap
import zipfile
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
import datasets
import datasets.config
@pytest.fixture(scope='''session''' )
def lowerCamelCase_ ( ):
'''simple doc... | 371 | from __future__ import annotations
from collections import Counter
from random import random
class __lowercase :
'''simple docstring'''
def __init__( self : List[Any] ):
UpperCamelCase__ = {}
def A_ ( self : ... | 35 | 0 |
"""simple docstring"""
def UpperCAmelCase__ (lowerCAmelCase_ ):
'''simple docstring'''
if number > 0:
raise ValueError("input must be a negative integer" )
__SCREAMING_SNAKE_CASE = len(bin(lowerCAmelCase_ )[3:] )
__SCREAMING_SNAKE_CASE = ... | 54 |
"""simple docstring"""
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.generation import DisjunctiveConstraint
@require_torch
class UpperCamelCase_ ( unittest.TestCase):
"""s... | 54 | 1 |
'''simple docstring'''
import unittest
from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ... | 370 |
'''simple docstring'''
import sys
from typing import Tuple
import numpy as np
import torch
from PIL import Image
from torch import nn
from transformers.image_utils import PILImageResampling
from utils import img_tensorize
class A :
def __init__( self , ... | 311 | 0 |
import os
import unittest
from transformers.models.phobert.tokenization_phobert import VOCAB_FILES_NAMES, PhobertTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class A( UpperCamelCase , unittest.TestCase ):
'''simple docstring'''
... | 204 |
from __future__ import annotations
import unittest
from transformers import BlenderbotConfig, BlenderbotTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import Conf... | 204 | 1 |
def _lowercase ( UpperCamelCase_ = 200 ) -> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = [1, 2, 5, 10, 20, 50, 100, 200]
SCREAMING_SNAKE_CASE__ = [0] * (pence + 1)
SCREAMING_SNAKE_CASE__ = 1 # base case: 1 way to make 0 pence
... | 169 |
import json
import os
import tempfile
import transformers
import datasets
from utils import generate_example_dataset, get_duration
__snake_case = 50_00_00
__snake_case ,__snake_case = os.path.split(__file__)
__snake_case = os.path.join(RESULTS_BASEPATH, """results""", RESUL... | 169 | 1 |
from typing import List, Optional, Union
import numpy as np
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging
lowerCamelCase__ = logging.get_logger(__name__)
... | 212 |
from __future__ import annotations
lowerCamelCase__ = list[list[int]]
# assigning initial values to the grid
lowerCamelCase__ = [
[3, 0, 6, 5, 0, 8, 4, 0, 0],
[5, 2, 0, 0, 0, 0, 0, 0, 0],
[0, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, 8, 0],
[9, 0, 0, 8, 6, 3, 0... | 212 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
A__ : List[Any] = {
'configuration_xlm': ['XLM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'XLMConfig', 'XLMOnnxConfig'],
'tokeni... | 366 |
"""simple docstring"""
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
A__ : Dict = logging.get_logger(__name... | 209 | 0 |
'''simple docstring'''
import argparse
import json
import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers import (
VideoMAEConfig,
VideoMAEForPreTraining,
VideoMAEForVideoClassification,
VideoMAEImageProcessor,
)
def lowerCAmelCase (__A... | 211 |
'''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 __snake_case( _lowerCAmelCase ) -> int:
snake_case__ : Optional[int] = ... | 35 | 0 |
# Usage:
# ./gen-card-allenai-wmt16.py
import os
from pathlib import Path
def lowerCamelCase_ ( UpperCamelCase__ : List[str], UpperCamelCase__ : Any, UpperCamelCase__ : List[str], UpperCamelCase__ : Dict ):
'''simple docstring'''
... | 367 | import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
lowercase = logging.get_logger(__name__)
class __lowercase ( A ):
'''simple docstring'''
def __init__( self : Any , *_a : Optional[A... | 35 | 0 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class SCREAMING_SNAKE_CASE_ ( metaclass=__a ):
"""simple docstring"""
__lowercase : List[str] = ['''torch''']
def __init__( self , *lowerCAmelCase__ , **lowerCAmelCase__):
... | 100 |
'''simple docstring'''
# Lint as: python3
import itertools
import os
import re
a : Tuple = re.compile(R"([A-Z]+)([A-Z][a-z])")
a : Union[str, Any] = re.compile(R"([a-z\d])([A-Z])")
a : str = re.compile(R"(?<!_)_(?!_)")
a : List[Any] = re.compile(R"(_{2,})")
a : ... | 311 | 0 |
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union
import numpy as np
import PIL
from PIL import Image
from ...utils import BaseOutput, is_torch_available, is_transformers_available
@dataclass
class __a ( __UpperCamelCase ):
__lower... | 288 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__lowerCAmelCase = logging.get_logger(__name__)
__lowerCAmelCase = '... | 288 | 1 |
import datasets
from .nmt_bleu import compute_bleu # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py
_lowerCAmelCase : Union[str, Any] = "\\n@INPROCEEDINGS{Papineni02bleu:a,\n author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},\n title = {B... | 169 |
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, ... | 169 | 1 |
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
from diffusers.utils imp... | 328 |
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 = '▁'
_UpperCAmelCase = {'vocab_file': 'spiece.model'}
_UpperCAmelCase = ... | 328 | 1 |
"""simple docstring"""
import inspect
import os
import unittest
from dataclasses import dataclass
import torch
from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subpr... | 33 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_a = logging.get_logger(__name__)
_a = {
"camembert-base": "https://huggingface.co/camembert-base/resolve/mai... | 209 | 0 |
"""simple docstring"""
def a__ ( __SCREAMING_SNAKE_CASE = 5_0 ) -> int:
__lowerCAmelCase: List[str] = [1] * (length + 1)
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_start in range(row_length - tile_length + 1 ):
... | 108 |
"""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
from ..utils.vers... | 108 | 1 |
import copy
import os
from typing import TYPE_CHECKING, List, Union
if TYPE_CHECKING:
pass
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCAmelCase : Optional[int] = logging.get_logger(__name__)
__lowerCAmelCase : Tuple ... | 88 |
'''simple docstring'''
from PIL import Image
def __snake_case( _lowerCAmelCase , _lowerCAmelCase ) -> Image:
def brightness(_lowerCAmelCase ) -> float:
return 128 + level + (c - 128)
if not -255.0 <= level <= 255.0:
raise ValueError("""level must be ... | 35 | 0 |
'''simple docstring'''
import importlib.util
import os
import platform
from argparse import ArgumentParser
import huggingface_hub
from .. import __version__ as version
from ..utils import (
is_accelerate_available,
is_flax_available,
is_safetensors_available,
is_tf_available,
is_torch_available,... | 274 | '''simple docstring'''
from typing import Optional, Union
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin
class __SCREAMING_SNAKE_CASE ( lowerCamelCase_ , lowerCamelCase_ ):
'''simp... | 274 | 1 |
"""simple docstring"""
import argparse
import tensorflow as tf
import torch
from transformers import BertConfig, BertForMaskedLM
from transformers.models.bert.modeling_bert import (
BertIntermediate,
BertLayer,
BertOutput,
BertPooler,
BertSelfAttention,
BertSelfOutput,
)
... | 288 |
"""simple docstring"""
import logging
from transformers.configuration_utils import PretrainedConfig
UpperCAmelCase__ = logging.getLogger(__name__)
class lowerCAmelCase__ ( A_ ):
__a = """masked_bert"""
def __init__( self : Union[str... | 288 | 1 |
import argparse
import re
from pathlib import Path
import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor
from transformers import (
EfficientFormerConfig,
EfficientFormerForImageClassificationWithTeacher,
EfficientFormerImag... | 369 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
SCREAMING_SNAKE_CASE :Any = {
'configuration_rag': ['RagConfig'],
'retrieval_rag': ['RagRetriever'],
'tokenization_rag': ['RagTokenizer'],
}
try:
... | 124 | 0 |
import unittest
import numpy as np
import torch
from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
"""simple docstring"""
def A__ ( self )-> List[str]:
... | 328 |
from __future__ import annotations
from collections.abc import Callable
def A_ ( snake_case : Callable[[int | float], int | float] , snake_case : int | float , snake_case : int | float , snake_case : int = 100 , ) ... | 328 | 1 |
class A :
"""simple docstring"""
def __init__(self ):
__lowercase= """"""
__lowercase= """"""
__lowercase= []
def _A (self , lowerCAmelCase , lowerCAmelCase ):
if m == -1:
return n + 1
elif... | 371 |
# 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... | 304 | 0 |
"""simple docstring"""
import datasets
from .evaluate import evaluate
lowerCAmelCase__ = '''\
@article{hendrycks2021cuad,
title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},
author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},
journal={ar... | 108 |
"""simple docstring"""
class SCREAMING_SNAKE_CASE__ :
"""simple docstring"""
def __init__( self , snake_case__ = "" , snake_case__ = False ):
"""simple docstring"""
lowerCAmelCase : dict[str, RadixNode] = {}
... | 108 | 1 |
def lowerCAmelCase_ ( __lowerCAmelCase )-> int:
'''simple docstring'''
assert column_title.isupper()
UpperCAmelCase : str =0
UpperCAmelCase : Optional[Any] =len(__lowerCAmelCase ) - 1
UpperCAmelCase : List[Any] =0
... | 78 | from collections import defaultdict
from pathlib import Path
import pandas as pd
from rouge_cli import calculate_rouge_path
from utils import calculate_rouge
__snake_case = [
'''Prosecutor: "No videos were used in the crash investigation" German papers say they saw a cell phone video of the'''
'''... | 78 | 1 |
import math
from collections.abc import Callable
def lowerCamelCase_ ( _a , _a , _a ):
"""simple docstring"""
lowerCAmelCase__ : Any = xa
lowerCAmelCase__ : List[str] = xa
while True:
if x_n == x_na or funct... | 131 |
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ ) -> list[int]:
'''simple docstring'''
if length <= 0 or not isinstance(UpperCamelCase__ , UpperCamelCase__ ):
raise ValueError('''Length must be a positive integer.''' )
return [n * (2 * n - 1) for n in range(UpperCamelCase__ ... | 273 | 0 |
"""simple docstring"""
UpperCAmelCase_ : Optional[int] = {
"""A""": """.-""", """B""": """-...""", """C""": """-.-.""", """D""": """-..""", """E""": """.""", """F""": """..-.""", """G""": """--.""",
"""H""": """....""", """I""": """..""", """J""": """.---""", """K""": """-.-""... | 352 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vis... | 318 | 0 |
'''simple docstring'''
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class lowercase_ (lowerCamelCase__ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[str] = ['image_processor', 'tokenizer']
SCREAMING_SNAKE_CASE... | 104 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowerCamelCase : Tuple = {
'configuration_transfo_xl': ['TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TransfoXLConfig'],
'tokenization_transfo_xl': ... | 124 | 0 |
'''simple docstring'''
import argparse
import json
import os
from tensorflow.core.protobuf.saved_model_pba import SavedModel
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
_SCREAMING_SNAKE_CASE = ... | 217 |
'''simple docstring'''
def _lowerCAmelCase ( lowerCamelCase_ : Union[str, Any] ):
__lowercase = 1
__lowercase = 2
while i * i <= n:
__lowercase = 0
while n % i == 0:
n //= i
multiplicity += 1
n_divisor... | 217 | 1 |
def A ( _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : Optional[int] = set()
# edges = list of graph's edges
_lowerCAmelCase : Dict = get_edges(_lowerCamelCase )
# While there are still elements in edges list, tak... | 36 |
'''simple docstring'''
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=UpperCamelCase)
class snake_case__ ( UpperCamelCase):
a_ = field(default="language-modeling" , ... | 304 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
SCREAMING_SNAKE_CASE : Dict = {
'''configuration_roc_bert''': ['''ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RoCBertConf... | 317 |
"""simple docstring"""
from math import isqrt
def __UpperCAmelCase ( snake_case_ : int ) -> list[int]:
"""simple docstring"""
_lowerCAmelCase = [True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
... | 317 | 1 |
"""simple docstring"""
import unittest
from transformers import MPNetConfig, 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, ids_tensor, random_att... | 78 |
"""simple docstring"""
from __future__ import annotations
def _lowerCAmelCase ( lowercase_ , lowercase_ , lowercase_ ):
UpperCAmelCase = list(range(len(lowercase_ ) ) )
UpperCAmelCase = [v / w for v, w in zip(lowercase_ , lowercase_ ... | 78 | 1 |
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inpu... | 162 |
def a__ ( A__ ):
SCREAMING_SNAKE_CASE_ : Optional[Any] = 0
SCREAMING_SNAKE_CASE_ : Union[str, Any] = len(A__ )
for i in range(n - 1 ):
for j in range(i + 1, A__ ):
if arr[i] > arr[j]:
num_inversions... | 162 | 1 |
from typing import List, Optional, Tuple, Union
import torch
from torch import nn
from torch.nn import CrossEntropyLoss
from ... import AutoBackbone
from ...modeling_outputs import SemanticSegmenterOutput
from ...modeling_utils import PreTrainedModel
from ...utils import add_start_docstrings, add_start_docstrings_to... | 303 |
'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from itertools import chain
from typing import Optional, Union
import datasets
import numpy as np
import torch
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
Aut... | 318 | 0 |
'''simple docstring'''
import argparse
from collections import defaultdict
import yaml
A__ : str ='''docs/source/en/_toctree.yml'''
def UpperCamelCase__ ( lowerCAmelCase ) -> str:
"""simple docstring"""
_lowerCAmelCase ... | 370 |
'''simple docstring'''
def UpperCamelCase__ ( lowerCAmelCase , lowerCAmelCase ):
"""simple docstring"""
if not (isinstance(lowerCAmelCase , lowerCAmelCase ) and isinstance(lowerCAmelCase , lowerCAmelCase )):
ra... | 220 | 0 |
"""simple docstring"""
def a__ ( __SCREAMING_SNAKE_CASE ) -> list[list[int]]:
__lowerCAmelCase: Any = []
if len(__SCREAMING_SNAKE_CASE ) == 1:
return [nums.copy()]
for _ in range(len(__SCREAMING_SNAKE_CASE ) ):
__lowerCAmelCase: int = nums.pop(0 )
... | 217 |
"""simple docstring"""
__A = {
"A": ["B", "C", "E"],
"B": ["A", "D", "E"],
"C": ["A", "F", "G"],
"D": ["B"],
"E": ["A", "B", "D"],
"F": ["C"],
"G": ["C"],
}
def a__ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE... | 217 | 1 |
"""simple docstring"""
class UpperCamelCase_ :
def __init__( self : Union[str, Any] , lowerCAmelCase_ : str , lowerCAmelCase_ : Dict , lowerCAmelCase_ : List[str] ) -> Tuple:
UpperCAmelCase_ : Optional[int] = None
UpperCAmelCas... | 359 |
"""simple docstring"""
import re
import string
import numpy as np
import datasets
lowerCamelCase_ = '''
Returns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.
'''
lowerCamelCase_ = '''
Args... | 253 | 0 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a__ = logging.get_logger(__name__)
a__ = {
"""xlm-roberta-base""": """https://huggingface.co/xlm-roberta-base/re... | 317 |
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_indic... | 317 | 1 |
"""simple docstring"""
from __future__ import absolute_import, division, print_function, unicode_literals
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers import RobertaConfig
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from tra... | 364 | """simple docstring"""
import importlib.metadata
import operator
import re
import sys
from typing import Optional
from packaging import version
UpperCAmelCase = {
"""<""": operator.lt,
"""<=""": operator.le,
"""==""": operator.eq,
"""!=""": operator.ne,
""">=""": operator.ge,
""">""":... | 54 | 0 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils... | 162 |
'''simple docstring'''
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> list[list[float]]:
A_ = []
for data in source_data:
for i, el in enumerate(UpperCAmelCase__ ):
if len(UpperCAmelCase__ ) < i + 1:
data_lists.appen... | 162 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
snake_case_ = logging.get_logger(__name__)
snake_case_ = {
"""facebook/c... | 364 |
"""simple docstring"""
from typing import Callable, List, Optional, Union
import PIL
import torch
from transformers import (
CLIPImageProcessor,
CLIPSegForImageSegmentation,
CLIPSegProcessor,
CLIPTextModel,
CLIPTokenizer,
)
from diffusers import DiffusionPipeline
from dif... | 181 | 0 |
"""simple docstring"""
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.skipU... | 183 |
"""simple docstring"""
_UpperCamelCase : List[str] = {
'meter': 'm',
'kilometer': 'km',
'megametre': 'Mm',
'gigametre': 'Gm',
'terametre': 'Tm',
'petametre': 'Pm',
'exametre': 'Em',
'zettametre': 'Zm',
'yottametre': 'Ym',
}
# Exponent of the factor(meter)
_UpperCame... | 220 | 0 |
import argparse
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline
if __name__ == "__main__":
_SCREAMING_SNAKE_CASE : Any = argparse.ArgumentParser()
parser.add_argument('''--dump_path''... | 218 |
def UpperCAmelCase_ ( _A ):
'''simple docstring'''
if not isinstance(_A , _A ):
raise ValueError('''multiplicative_persistence() only accepts integral values''' )
if num < 0:
raise ValueError('''multiplicative_persistence() does not accept negati... | 218 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
UpperCAmelCase__ : Union[str, Any] = {
'configuration_data2vec_audio': ['DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP', ... | 25 |
import argparse
import gc
import json
import os
import re
import torch
from huggingface_hub import hf_hub_download
from transformers import AutoModelForCausalLM, AutoTokenizer, PreTrainedTokenizerFast, RwkvConfig
from transformers.modeling_utils import WEIGHTS_INDEX_NAME, shard_checkpoint
lowerCAmelCase : ... | 253 | 0 |
import warnings
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__A =logging.get_logger(__name__)
class _SCREAMING_SNAKE_CASE ( snake_case_ ):
lowerCAmelCase__ = ['input_ids', 'attention_... | 47 |
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.models import Sequential
if __name__ == "__main__":
__A =pd.read_csv('''sample_data.csv''', header=None)
__A =df.shape[:1][0]
# If you're using som... | 47 | 1 |
'''simple docstring'''
# limitations under the License.
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from .pipelines import DiffusionPipeline, ImagePipelineOutput # noqa: F401
from .u... | 79 |
"""simple docstring"""
def UpperCAmelCase__ (lowerCAmelCase_ ):
'''simple docstring'''
if number > 0:
raise ValueError("input must be a negative integer" )
__SCREAMING_SNAKE_CASE = len(bin(lowerCAmelCase_ )[3:] )
__SCREAMING_SNAKE_CASE = ... | 54 | 0 |
"""simple docstring"""
def UpperCAmelCase__ (lowerCAmelCase_ ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = 0
for ch in input_str:
__SCREAMING_SNAKE_CASE = ord(lowerCAmelCase_ )
__SCREAMING_SNAKE_CASE = ... | 359 |
"""simple docstring"""
from __future__ import annotations
def UpperCAmelCase__ (lowerCAmelCase_ , lowerCAmelCase_ ):
'''simple docstring'''
print(f"""Vertex\tShortest Distance from vertex {src}""" )
for i, d in enumerate(lowerCAmelCase_ ):
prin... | 195 | 0 |
import absl # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import six # noqa: F401 # Here to have a nice ... | 76 |
'''simple docstring'''
import coval # From: git+https://github.com/ns-moosavi/coval.git # noqa: F401
from coval.conll import reader, util
from coval.eval import evaluator
import datasets
UpperCamelCase__ = datasets.logging.get_logger(__name__)
UpperCamelCase__ = '''\
@In... | 181 | 0 |
'''simple docstring'''
import heapq
def __magic_name__ ( A ) -> set[int]:
snake_case = []
# for each node and his adjacency list add them and the rank of the node to queue
# using heapq module the queue will be filled like a Priority Queue
# heapq works with a min priority queu... | 354 |
'''simple docstring'''
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
fr... | 332 | 0 |
import argparse
import os
import re
_lowerCAmelCase : Tuple = "src/transformers/models/auto"
# re pattern that matches mapping introductions:
# SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
_lowerCAmelCase : Optional[int] = re.compile(r"[A-Z_... | 218 |
import argparse
import re
from pathlib import Path
import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor
from transformers import (
EfficientFormerConfig,
EfficientFormerForImageClassificationWithTeacher,
Effi... | 218 | 1 |
import unittest
from datasets import load_dataset
from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class lowerCAmelCase__( __lowercase , unittest.TestCase ... | 364 |
from unittest.mock import Mock, patch
from file_transfer.send_file import send_file
@patch("socket.socket" )
@patch("builtins.open" )
def lowerCamelCase__ (__lowerCamelCase, __lowerCamelCase ):
# ===== initialization =====
_SCREAMING_SNAKE_CASE : List[Any] = Mock(... | 325 | 0 |
'''simple docstring'''
class A__ : # Public class to implement a graph
def __init__( self : List[Any] , _a : int , _a : int , _a : list[list[bool]] ) -> None:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =row
... | 47 |
'''simple docstring'''
import numpy as np
from scipy.spatial.distance import cdist
from sklearn.metrics import fa_score
import datasets
lowerCamelCase : List[Any] = "\\n @inproceedings{kakwani2020indicnlpsuite,\n title={{IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained ... | 47 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ : Union[str, Any] = logging.get_logger(__name__)
lowercase__ : str = {
'''uclanlp/visualbert-vqa''': '''https://huggingface.co/uclanlp/visualbert... | 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 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
lowercase_ = {'configuration_fnet': ['FNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FNetConfig']}
try... | 205 |
import os
from datetime import datetime as dt
from github import Github
UpperCAmelCase = [
'''good first issue''',
'''good second issue''',
'''good difficult issue''',
'''enhancement''',
'''new pipeline/model''',
'''new scheduler''',
'''wip''',
]
def UpperCAmelCase_ (... | 195 | 0 |
from tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch
from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER, require_tf, require_torch
@require_torch
@... | 300 |
import unittest
from transformers import (
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
Pipeline,
ZeroShotClassificationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow
fr... | 300 | 1 |
'''simple docstring'''
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
from .record_evaluation import evaluate as evaluate_record
__snake_case ="""\
@article{wang2019superglue,
title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding ... | 4 |
"""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, PyTorchBe... | 332 | 0 |
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__ ):
... | 225 |
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__ ):
... | 225 | 1 |
"""simple docstring"""
from math import pi
def _A ( UpperCamelCase_ : int, UpperCamelCase_ : int) -> float:
'''simple docstring'''
return 2 * pi * radius * (angle / 360)
if __name__ == "__main__":
print(arc_length(90, 10))
| 17 |
import enum
import os
from hashlib import shaaaa
from typing import Optional
from .. import config
from .logging import get_logger
SCREAMING_SNAKE_CASE__ = get_logger(__name__)
class A__ ( enum.Enum ):
lowerCAmelCase__ : Dict = "all_checks"
lowerCAmelCase__ : ... | 325 | 0 |
'''simple docstring'''
from __future__ import annotations
import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module
import numpy as np
from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_availab... | 354 |
'''simple docstring'''
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
fr... | 123 | 0 |
'''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__ : List[Any] = {
'''linear''': PIL.Image.Resampli... | 190 |
'''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 ):
lower... | 190 | 1 |
import json
import logging
import os
import socket
import git
import numpy as np
import torch
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO,
)
SCREAMING_SNAKE_CASE : Optional[Any] ... | 357 |
import os
import re
import warnings
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_ta import TaTokenize... | 252 | 0 |
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