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 |
|---|---|---|---|---|
__lowerCAmelCase : int = 9.80_665
def UpperCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = g ) -> float:
if fluid_density <= 0:
raise ValueError('''Impossible fluid density''' )
if volume < 0:
raise ValueError('''Impossible... | 156 |
from __future__ import annotations
from PIL import Image
# Define glider example
__lowerCAmelCase : Optional[int] = [
[0, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0],
[1, 1, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0... | 156 | 1 |
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device
__UpperCamelCase : Union[str, Any] = False
class __magic_name__ ( unittest... | 51 |
def _a ( SCREAMING_SNAKE_CASE : int ):
"""simple docstring"""
UpperCamelCase__ : List[str] = generate_pascal_triangle(SCREAMING_SNAKE_CASE )
for row_idx in range(SCREAMING_SNAKE_CASE ):
# Print left spaces
for _ in range(num_rows - row_idx - 1 )... | 51 | 1 |
'''simple docstring'''
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_docst... | 272 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__lowercase = {
'''configuration_resnet''': ['''RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ResNe... | 272 | 1 |
"""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
... | 369 |
"""simple docstring"""
import os
from collections.abc import Iterator
def _snake_case ( UpperCamelCase : str = "." ):
for dir_path, dir_names, filenames in os.walk(UpperCamelCase ):
UpperCAmelCase : List[Any] = [d for d in dir_names if d != """scripts""" and d[0] not in... | 76 | 0 |
from typing import Any, Callable, Dict, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiff... | 114 | '''simple docstring'''
import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel
class a__ ( UpperCAmelCase__ ):
lowerCamelCase : Dict ="M-CLIP"
def __init__( self : Tuple , a : Optional[int]=10_24 , a : Tuple=7_68 , **a : ... | 67 | 0 |
"""simple docstring"""
from typing import List, Optional, Union
import torch
from transformers import (
XLMRobertaTokenizer,
)
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import... | 365 |
"""simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_SCREAMING_SNAKE_CASE : Tuple = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE : List[Any] = {
"""microsoft/unispeech-large-1500h-cv""": (
... | 157 | 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
__lowerCAmelCase : Any = logging.get_logger(__name__)
__lowerCAmelCase : List[str] ... | 107 |
from __future__ import annotations
from scipy.special import comb # type: ignore
class A_ :
def __init__( self : List[str] ,SCREAMING_SNAKE_CASE__ : list[tuple[float, float]]):
__lowerCamelCase : Union[str, Any] = list_of_points
# Degree d... | 73 | 0 |
'''simple docstring'''
from typing import List
import numpy as np
def __lowerCamelCase ( __snake_case : dict ) -> int:
"""simple docstring"""
A__ : List[Any] ={key: len(__snake_case ) for key, value in gen_kwargs.items() if isinstance(__snake_case, __sn... | 136 |
'''simple docstring'''
import torch
from torch import nn
class lowerCamelCase ( nn.Module ):
'''simple docstring'''
def __init__( self : Any , lowerCAmelCase_ : List[str] , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : Dict , lowerCAmelCase... | 136 | 1 |
"""simple docstring"""
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
import torch
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_ext... | 183 |
"""simple docstring"""
import importlib
import shutil
import threading
import warnings
from typing import List
import fsspec
import fsspec.asyn
from . import compression
from .hffilesystem import HfFileSystem
_SCREAMING_SNAKE_CASE : Any = importlib.util.find... | 183 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_A = {
'''configuration_clap''': [
'''CLAP_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''ClapAudioConfig''',
'''ClapConfig''',
'''ClapTextConfig''',
],
'''... | 358 |
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 | 0 |
import numpy as np
# Importing the Keras libraries and packages
import tensorflow as tf
from tensorflow.keras import layers, models
if __name__ == "__main__":
# Initialising the CNN
# (Sequential- Building the model layer by layer)
__UpperCAmelCase = models.Sequential()
#... | 29 |
from math import sqrt
def UpperCAmelCase_ ( __UpperCAmelCase : int ) -> bool:
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiple... | 225 | 0 |
"""simple docstring"""
import dataclasses
import re
import string
from typing import Any, Dict, Iterator, List, Mapping, Optional, Sequence, Tuple
import numpy as np
from . import residue_constants
snake_case_ = Mapping[str, np.ndarray]
snake_case_ = Mapping[str, Any] # Is a... | 181 |
"""simple docstring"""
import math
import flax.linen as nn
import jax.numpy as jnp
def _lowerCAmelCase ( lowercase_ , lowercase_ , lowercase_ = 1 , lowercase_ = 1 , lowercase_ = 1.0e4 , lowercase_ = False , lowercase_ = 1.0 , ):
assert timesteps.... | 181 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
UpperCamelCase__: Union[str, Any] = {
"configuration_squeezebert": [
"SQUEEZEBERT_PRETRAINED_CONFIG_... | 23 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCamelCase__: str = {
"configuration_lxmert": ["LXMERT_PR... | 23 | 1 |
"""simple docstring"""
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SegformerConfig,
SegformerForImageClassification,
SegformerFor... | 367 |
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 = {
"distilbert-base-uncased": "https://huggingface.co/distilbert-base-uncased... | 273 | 0 |
"""simple docstring"""
import os
import re
import warnings
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
if TYPE_CHECKING:
from ...tokenization_utils_base import TextInpu... | 91 |
import os
from collections import namedtuple
import pytest
from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict
A_ : Any = namedtuple(
'_TestCommandArgs',
[
'dataset',
... | 192 | 0 |
"""simple docstring"""
import argparse
import torch
from transformers import (
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaForAudioFrameClassification,
WavaVecaForSequenceClassification,
WavaVecaForXVector,
logging,
)
logging.set_verbosity_info()
UpperCAmelCase : Tuple ... | 370 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import FunnelConfig, is_tf_available
from transformers.testing_utils import require_tf
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, rand... | 320 | 0 |
'''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 To... | 298 |
'''simple docstring'''
import inspect
import warnings
from typing import Any, Dict, Optional, Union
from packaging import version
def a_ ( *_lowerCAmelCase ,_lowerCAmelCase = None ,_lowerCAmelCase=True ,_lowerCAmelCase=2 ) -> List[str]:
fr... | 208 | 0 |
'''simple docstring'''
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
f... | 156 |
'''simple docstring'''
from typing import List, Union
import numpy as np
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import... | 156 | 1 |
'''simple docstring'''
def lowerCAmelCase_ ( snake_case__ ):
'''simple docstring'''
if not nums: # Makes sure that the list is not empty
raise ValueError('''List is empty''' )
A : int = sum(SCREAMING_SNAKE_CASE_ ) / len(SCREAMING... | 3 |
import unittest
from transformers import AlbertTokenizer, AlbertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
lowerCamelCase__ = get_tests_dir("""fixtures/spiece.mod... | 212 | 0 |
import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional
import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto import tqdm
import transformers
from transformers import AutoCon... | 362 |
import argparse
from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging
logging.set_verbosity_info()
def __lowerCamelCase ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase... | 200 | 0 |
def A (__A : list[int] , __A : int ) -> bool:
"""simple docstring"""
UpperCAmelCase_ = len(__A )
UpperCAmelCase_ = [[False] * (required_sum + 1) for _ in range(arr_len + 1 )]
# for each ar... | 51 |
import argparse
import os
import pickle
import sys
import torch
from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl ... | 51 | 1 |
def snake_case_(_UpperCamelCase ) -> int:
"""simple docstring"""
if not numbers:
return 0
if not isinstance(_UpperCamelCase , (list, tuple) ) or not all(
isinstance(_UpperCamelCase , _UpperCamelCase ) for number in numbers ):
... | 358 |
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,
AutoTokenizer,
... | 278 | 0 |
"""simple docstring"""
def _snake_case ( lowercase__ : int ) -> Dict:
'''simple docstring'''
if collection == []:
return []
# get some information about the collection
lowerCAmelCase_ :List[Any] = len(lowercase__ )
lowerCAmelCase_ ... | 84 |
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 (
center_crop,
get_res... | 76 | 0 |
'''simple docstring'''
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union
# Build the list of all feature extractors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_... | 357 |
'''simple docstring'''
import copy
import os
import cva
import numpy as np
from matplotlib import pyplot as plt
class a__:
def __init__( self : Optional[int] ):
a : int = ''
a : List[str] = ''
a : int = ... | 96 | 0 |
def lowerCAmelCase__ ( _SCREAMING_SNAKE_CASE : Any ):
"""simple docstring"""
if p < 2:
raise ValueError("""p should not be less than 2!""" )
elif p == 2:
return True
__a = 4
__a = (1 << p) - 1
for _ in range(p - 2 ):
... | 302 | import argparse
import os
import re
_snake_case = '''src/transformers/models/auto'''
# re pattern that matches mapping introductions:
# SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
_snake_case = re.compile(r'''[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDi... | 157 | 0 |
"""simple docstring"""
import math
import qiskit
def __UpperCAmelCase ( snake_case_ : int = 1 , snake_case_ : int = 1 , snake_case_ : int = 1 ) -> qiskit.result.counts.Counts:
"""simple docstring"""
if (
isinstance(snake_case_ , s... | 371 |
"""simple docstring"""
from operator import delitem, getitem, setitem
import pytest
from data_structures.hashing.hash_map import HashMap
def __UpperCAmelCase ( snake_case_ : Union[str, Any] ) -> Dict:
"""simple docstring"""
return getitem, k
def __UpperCAm... | 317 | 0 |
"""simple docstring"""
from __future__ import annotations
import math
import random
from collections.abc import Collection
from typing import overload
class SCREAMING_SNAKE_CASE__ :
def __init__( self : List[str] , lowerCAmelCase_ : Collection[float] | None = None):
... | 136 |
"""simple docstring"""
from copy import deepcopy
import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
... | 136 | 1 |
"""simple docstring"""
from __future__ import annotations
UpperCAmelCase: str = [True] * 1_000_001
UpperCAmelCase: List[Any] = 2
while i * i <= 1_000_000:
if seive[i]:
for j in range(i * i, 1_000_001, i):
UpperCAmelCase: Optional[Any] = False... | 336 |
"""simple docstring"""
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase ):
return " ".join(input_str.split()[::-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 336 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase_ : Optional[int] = {
"""configuration_clipseg""": [
"""CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""CLIPSegConfig""",
... | 200 | """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 | 0 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import EsmConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ... | 369 |
'''simple docstring'''
from collections import UserDict
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available... | 37 | 0 |
'''simple docstring'''
import os
def a__ ( lowerCAmelCase__ ) -> int:
UpperCAmelCase__ : Any = len(grid[0] )
UpperCAmelCase__ : Tuple = len(lowerCAmelCase__ )
UpperCAmelCase__ : Any = 0
UpperCA... | 181 |
'''simple docstring'''
from typing import List, Optional, Tuple, Union
import torch
from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
UpperCamelCase__ = logging.get_logger(__name__) # pylint: disable=invalid-name
clas... | 181 | 1 |
'''simple docstring'''
import unittest
from transformers.utils.backbone_utils import (
BackboneMixin,
get_aligned_output_features_output_indices,
verify_out_features_out_indices,
)
class a ( unittest.TestCase ):
def A_ ( self : Optional[int] ):
snake_cas... | 363 |
'''simple docstring'''
import os
import re
import sys
import traceback
import warnings
from pathlib import Path
from typing import Dict, Optional, Union
from uuid import uuida
from huggingface_hub import HfFolder, ModelCard, ModelCardData, hf_hub_download, whoami
from huggingface_hub.file_download import REGEX_C... | 72 | 0 |
import os
import unittest
from transformers.models.bartpho.tokenization_bartpho import VOCAB_FILES_NAMES, BartphoTokenizer
from transformers.testing_utils import get_tests_dir
from ...test_tokenization_common import TokenizerTesterMixin
__A = get_tests_dir("fixtures/test_sentencepiece_bpe.model")
... | 10 |
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.utils.t... | 273 | 0 |
'''simple docstring'''
from collections.abc import Callable
import numpy as np
def _lowerCAmelCase ( __snake_case : Callable , __snake_case : float , __snake_case : float , __snake_case : float , __snake_case : float ) -... | 356 |
'''simple docstring'''
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class SCREAMING_SNAKE_CASE (a__ ):
lowerCAmelCase = ['''image_processor''', '''tokenizer''']
lowerCAmelCase = '''AutoImageProcessor''... | 190 | 0 |
'''simple docstring'''
import argparse
from typing import List
import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset
# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
from torch.optim import AdamW
from torch... | 89 |
"""simple docstring"""
import tempfile
import unittest
from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
torch_device,
)
from ...generation.test_utils import GenerationTesterMixin
... | 320 | 0 |
import os
import unittest
from transformers.models.bartpho.tokenization_bartpho import VOCAB_FILES_NAMES, BartphoTokenizer
from transformers.testing_utils import get_tests_dir
from ...test_tokenization_common import TokenizerTesterMixin
lowerCAmelCase__ = get_tests_dir('fixtures/test_sentencepiece_bpe.m... | 119 | import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert import BertTokenizer
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAme... | 119 | 1 |
import json
import os
import shutil
import tempfile
from unittest import TestCase
from transformers import BartTokenizer, BartTokenizerFast, DPRQuestionEncoderTokenizer, DPRQuestionEncoderTokenizerFast
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bert.tokenizati... | 156 |
from __future__ import annotations
from PIL import Image
# Define glider example
__lowerCAmelCase : Optional[int] = [
[0, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0],
[1, 1, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0... | 156 | 1 |
'''simple docstring'''
from itertools import zip_longest
import requests
from bsa import BeautifulSoup
from pandas import DataFrame
def lowercase__ ( __UpperCamelCase = "laptop" )-> DataFrame:
UpperCamelCase = F"https://www.amazon.in/laptop/s?k={product}"
... | 183 |
'''simple docstring'''
import importlib
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Union
import torch
from ..utils import BaseOutput
SCREAMING_SNAKE_CASE__ = 'scheduler_config.json'
class a_ ( ... | 183 | 1 |
from argparse import ArgumentParser
from .env import EnvironmentCommand
def A_ ( ) -> str:
UpperCamelCase : List[str] = ArgumentParser("Diffusers CLI tool" , usage="diffusers-cli <command> [<args>]" )
UpperCamelCase : Optional[Any] = parser.add_subparsers(... | 52 |
'''simple docstring'''
import unittest
from knapsack import greedy_knapsack as kp
class lowercase__ ( unittest.TestCase ):
'''simple docstring'''
def UpperCAmelCase_ ( self ):
_SCREAMING_SNAKE_CASE : str = [10, 20, 30, 40, 50, 60]
... | 200 | 0 |
import copy
import random
from transformers import CLIPTokenizer
class a_ ( a__ ):
"""simple docstring"""
def __init__( self , *_lowerCamelCase , **_lowerCamelCase ) ->List[Any]:
super().__init__(*_lowerCamelCase , **_lowerCamelCase )
... | 19 |
from math import pi, sqrt, tan
def UpperCAmelCase_( a__ ):
"""simple docstring"""
if side_length < 0:
raise ValueError('''surface_area_cube() only accepts non-negative values''' )
return 6 * side_length**2
def UpperCAmelCase_( a__ , a__ , a__ ):
""... | 19 | 1 |
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_uti... | 73 |
import io
import json
import fsspec
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.json import JsonDatasetReader, JsonDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def __UpperCamelCase ( _A... | 278 | 0 |
# 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/licenses/LICENSE-2.0
#
# Unless required by applicabl... | 371 | import warnings
from diffusers import StableDiffusionImgaImgPipeline # noqa F401
warnings.warn(
"The `image_to_image.py` script is outdated. Please use directly `from diffusers import"
" StableDiffusionImg2ImgPipeline` instead."
)
| 305 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_A = {'configuration_ibert': ['IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'IBertConfig', 'IBertOnnxConfig']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailabl... | 62 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Iterator
from typing import Any
class lowerCAmelCase__ :
'''simple docstring'''
def __init__( self , lowercase ):
_lowerCamelCase : Any = da... | 96 | 0 |
from __future__ import annotations
def _A ( lowerCAmelCase_ : list[int] , lowerCAmelCase_ : int , lowerCAmelCase_ : int , lowerCAmelCase_ : int ):
"""simple docstring"""
if (direction == 1 and array[indexa] > array[indexa])... | 221 |
import random
def _A ( lowerCAmelCase_ : list , lowerCAmelCase_ : List[str] ):
"""simple docstring"""
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ = [], [], []
for element in data:
if element < pivot:
... | 221 | 1 |
import gc
import unittest
import numpy as np
import torch
from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determi... | 119 |
import argparse
import os
from pathlib import Path
import fairseq
import torch
from packaging import version
from torch import nn
from transformers import (
BartConfig,
BartForConditionalGeneration,
BartForSequenceClassification,
BartModel,
BartTokenizer,
)
from transformer... | 317 | 0 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from diffusers import (
AudioDiffusionPipeline,
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
DiffusionPipeline,
Mel,
UNetaDConditionModel,
UNetaDModel,
)
from diffusers.utils import slow, torch_device
fro... | 367 | """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 ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging
... | 85 | 0 |
"""simple docstring"""
from math import sqrt
def _snake_case ( UpperCAmelCase_ : int ):
assert isinstance(UpperCAmelCase_ , UpperCAmelCase_ ) and (
number >= 0
), "'number' must been an int and positive"
A__ = True
# 0 and ... | 335 |
"""simple docstring"""
import math
import time
from typing import Dict, List, Optional
from torch.utils.data import Dataset
from transformers import SeqaSeqTrainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_devi... | 335 | 1 |
"""simple docstring"""
import unittest
from transformers import BertGenerationConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_mode... | 362 |
"""simple docstring"""
def snake_case_ ( A_ : list ):
'''simple docstring'''
_lowerCamelCase : Union[str, Any] = len(A_ )
for i in range(1, A_ ):
_lowerCamelCase : Tuple = collection[i]
_lowerCa... | 175 | 0 |
import csv
import tweepy
# Twitter API credentials
__lowerCamelCase : Dict = ''''''
__lowerCamelCase : Union[str, Any] = ''''''
__lowerCamelCase : Dict = ''''''
__lowerCamelCase : List[Any] = ''''''
def _snake_case ( lowerCAmelCase : str ):
"""simple d... | 18 |
'''simple docstring'''
import os
from bleurt import score # From: git+https://github.com/google-research/bleurt.git
import datasets
_lowerCAmelCase = datasets.logging.get_logger(__name__)
_lowerCAmelCase = '''\
@inproceedings{bleurt,
title={BLEURT: Learning Robust Metrics for Tex... | 37 | 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_tok... | 352 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE : Tuple = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE : Dict = {
"bigcode/gpt_bigcode-santacoder": "https://huggingface.co/bigcode/gpt_bigcode-santa... | 252 | 0 |
from itertools import product
def _UpperCAmelCase (UpperCamelCase__ : int , UpperCamelCase__ : int ):
_A : Dict = sides_number
_A : Any = max_face_number * dice_number
_A : Optional[int] = [0] * (max_total + ... | 11 |
"""simple docstring"""
def snake_case_ ( A_ : list[int], A_ : str ):
'''simple docstring'''
_lowerCamelCase : Tuple = int(A_ )
# Initialize Result
_lowerCamelCase : Dict = []
# Traverse through all deno... | 72 | 0 |
'''simple docstring'''
def __UpperCamelCase ( UpperCAmelCase ):
def merge(UpperCAmelCase , UpperCAmelCase ) -> list:
def _merge():
while left and right:
yield (left if left[0] <= right[0] else right).pop(0 )
yield from left
yield from right
return list(_merge() )
... | 214 | '''simple docstring'''
def __UpperCamelCase ( UpperCAmelCase ):
if not all(x.isalpha() for x in string ):
raise ValueError('''String must only contain alphabetic characters.''' )
lowercase__ : Tuple = sorted(string.lower() )
return len(UpperCAmelCase ) == len(set(UpperCA... | 214 | 1 |
import unittest
import numpy as np
import requests
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
... | 244 |
'''simple docstring'''
import os
import tempfile
import unittest
import numpy as np
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax, slow
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import... | 190 | 0 |
'''simple docstring'''
def __lowerCamelCase ( _lowercase ) -> int:
assert column_title.isupper()
UpperCAmelCase : Dict = 0
UpperCAmelCase : Tuple = len(_lowercase ) - 1
UpperCAmelCase : Tuple = 0
while index >=... | 338 |
'''simple docstring'''
from __future__ import annotations
# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
a : Optional[int] = 1_0
def __lowerCamelCase ( _lowercase , _lowercase , ... | 338 | 1 |
import unittest
import numpy as np
from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from ... | 119 |
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image
from transformers import (
Effic... | 119 | 1 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
if is_torch_available():
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
@require_torch
@require_sentenc... | 362 |
def __A ( __lowerCamelCase ) -> bool:
return number & 1 == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 347 | 0 |
"""simple docstring"""
import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor
def lowerCamelCase__ ... | 183 |
"""simple docstring"""
from __future__ import annotations
from typing import Generic, TypeVar
_SCREAMING_SNAKE_CASE : Optional[Any] = TypeVar('''T''')
class a ( Generic[T] ):
def __init__( self : List[str] ... | 183 | 1 |
"""simple docstring"""
def a__ ( _SCREAMING_SNAKE_CASE = 10 , _SCREAMING_SNAKE_CASE = 22 ):
"""simple docstring"""
UpperCamelCase = range(1 , _SCREAMING_SNAKE_CASE )
UpperCamelCase = range(1 , _SCREAMING_SNAKE_CASE )
return sum(
1 for power in po... | 244 |
"""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
from ...test_config... | 244 | 1 |
import inspect
import unittest
from huggingface_hub import hf_hub_download
from transformers import ConvNextConfig, UperNetConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import is_torch_available, is_vision_available
f... | 19 |
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel
from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import HeunDiscreteScheduler
from ...... | 19 | 1 |
import torch
from torch import nn
class __lowercase ( nn.Module ):
'''simple docstring'''
def __init__( self : Any , _a : Tuple , _a : str , _a : int , _a : int , _a : int=1 , _a : Tuple=Fa... | 35 | import numpy as np
from scipy.spatial.distance import cdist
from sklearn.metrics import fa_score
import datasets
lowercase = """\
@inproceedings{kakwani2020indicnlpsuite,
title={{IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language Models for I... | 35 | 1 |
import argparse
import torch
from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
__lowerCAmelCase : Tuple = logging.get_logger(__name__)
__lowerCAmelCase : List[Any] = [
... | 88 |
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_space_optuna,
defau... | 326 | 0 |
import argparse
import logging
import os
from datetime import datetime
import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from tqdm import tqdm
from transformers import GPTaLMHeadModel
UpperCAmelCase__ : Optional[int] =logging.g... | 262 |
import qiskit
def _lowercase ( _UpperCAmelCase , _UpperCAmelCase ) -> qiskit.result.counts.Counts:
lowerCamelCase =qiskit.Aer.get_backend("""aer_simulator""" )
# Create a Quantum Circuit acting on the q register
lowerCamelCase =qiskit.QuantumCircuit(_UpperCAmelCase ... | 262 | 1 |
"""simple docstring"""
import comet # From: unbabel-comet
import torch
import datasets
__lowerCamelCase = datasets.logging.get_logger(__name__)
__lowerCamelCase = "\\n@inproceedings{rei-EtAl:2020:WMT,\n author = {Rei, Ricardo and Stewart, Craig and Farinha, Ana C an... | 221 | """simple docstring"""
import inspect
import unittest
from huggingface_hub import hf_hub_download
from transformers import ConvNextConfig, UperNetConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import is_t... | 221 | 1 |
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,
BertTokenizer... | 355 | from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowercase_ = {"configuration_yolos": ["YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP", "YolosConfig", "YolosOnnxConfig"]}
try:
if not is_vision_available():
raise... | 20 | 0 |
import argparse
import torch
from transformers import GPTaLMHeadModel, RobertaForMaskedLM
if __name__ == "__main__":
UpperCamelCase = argparse.ArgumentParser(
description=(
"""Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transfer Learned"""
... | 186 |
'''simple docstring'''
import unittest
from transformers import (
MODEL_FOR_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_CAUSAL_LM_MAPPING,
TextGenerationPipeline,
logging,
pipeline,
)
from transformers.testing_utils import (
CaptureLogger,
is_pipeline_test,
require_accelerate,
require... | 85 | 0 |
'''simple docstring'''
import math
from numpy import inf
from scipy.integrate import quad
def UpperCAmelCase_ ( __lowercase : float ) -> float:
'''simple docstring'''
if num <= 0:
raise ValueError("math domain error" )
return quad(__lowercase , 0 ... | 364 |
'''simple docstring'''
from typing import Union
import fire
import torch
from tqdm import tqdm
def UpperCAmelCase_ ( __lowercase : str , __lowercase : str = "cpu" , __lowercase : Union[str, None] = None ) -> None:
'''simple docstring'''
... | 156 | 0 |
import unittest
import numpy as np
import timeout_decorator # noqa
from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_... | 306 | import asyncio
import os
import shutil
import subprocess
import sys
import tempfile
import unittest
from distutils.util import strtobool
from functools import partial
from pathlib import Path
from typing import List, Union
from unittest import mock
import torch
from ..state import AcceleratorState, PartialState
from ... | 175 | 0 |
def __lowerCamelCase ( a_ : int , a_ : bool = False ) -> Any:
if n == 2:
return True
if not n % 2 or n < 2:
return False
if n > 5 and n % 10 not in (1, 3, 7, 9): # can quickly check last digit
... | 357 |
"""simple docstring"""
def __lowerCamelCase ( a_ : str ) -> list:
return [
txt[:a] + txt[a].upper() + txt[a + 1 :]
for a in range(len(a_ ) )
if txt[a].isalpha()
]
if __name__ == "__main__":
__import__("do... | 239 | 0 |
import os
import numpy
import onnx
def __lowerCamelCase ( lowerCamelCase__ : str , lowerCamelCase__ : Optional[Any] ):
'''simple docstring'''
lowerCamelCase = a.name
lowerCamelCase = b.name
lowerCamelCase = """"""
lowerCamelCase ... | 252 |
import argparse
import shlex
import runhouse as rh
if __name__ == "__main__":
# Refer to https://runhouse-docs.readthedocs-hosted.com/en/latest/api/python/cluster.html#hardware-setup for cloud access
# setup instructions, if using on-demand hardware
# If user passes --user <user> --host <host> --key_path <... | 252 | 1 |
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 Ac... | 351 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
lowerCAmelCase = {
'''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/config.json''',
'''albert-large-v1''': '''https://huggingface... | 304 | 0 |
import math
def snake_case__ ( SCREAMING_SNAKE_CASE_ : int ):
'''simple docstring'''
lowercase__ : Optional[Any] = []
lowercase__ : str = 2
lowercase__ : Optional[Any] = int(math.sqrt(SCREAMING_SNAKE_CASE_ ) ) # Size of ever... | 214 |
import itertools
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union
import pandas as pd
import pyarrow as pa
import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast
from datasets.utils.py_ut... | 214 | 1 |
"""simple docstring"""
def a__ ( __lowercase ) -> int:
assert (
isinstance(__lowercase , __lowercase ) and number_of_steps > 0
), f"""number_of_steps needs to be positive integer, your input {number_of_steps}"""
if number_of_steps == 1:
return 1
... | 163 |
"""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
a_ = logging.get_logger(__name__)
a_ = ... | 163 | 1 |
def SCREAMING_SNAKE_CASE_ ( snake_case__ ) -> int:
assert column_title.isupper()
lowerCAmelCase = 0
lowerCAmelCase = len(snake_case__ ) - 1
lowerCAmelCase = 0
while index >= 0:
lowerCAmelCase = (ord(column_title[index] ... | 338 | 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 lowercase_ ( UpperCamelCase_ ):
"""simple d... | 338 | 1 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
'Salesforce/blip-vqa-base': 'https://huggingface.co/Salesforce/blip-vqa-base/resolve/m... | 222 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
enable_full_determini... | 222 | 1 |
def _UpperCAmelCase ( SCREAMING_SNAKE_CASE__ : Tuple ):
__UpperCamelCase =[0 for i in range(len(_SCREAMING_SNAKE_CASE ) )]
# initialize interval's left pointer and right pointer
__UpperCamelCase , __UpperCamelCase =0, 0
for i in range(1 ... | 62 |
"""simple docstring"""
# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
# python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't ... | 347 | 0 |
'''simple docstring'''
import random
from .binary_exp_mod import bin_exp_mod
def __lowerCAmelCase ( UpperCamelCase__ , UpperCamelCase__=10_00 ) -> Optional[int]:
"""simple docstring"""
if n < 2:
return False
if n % 2 == 0:
return n == 2
# this ... | 368 | '''simple docstring'''
import argparse
import fairseq
import torch
from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging
logging.set_verbosity_info()
__UpperCAmelCase =logging.get_logger(__name__)
__UpperCAmelCase ={
"post_extract_proj": ... | 237 | 0 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class __A( __lowerCamelCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = ["""image_processor""", """tokenizer"""]
SCREAMING_SNAKE_CASE__ = """CLIPImageProcessor"""
... | 244 |
import argparse
import os
import pickle
import sys
import torch
from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl import CORPUS_NAME... | 244 | 1 |
'''simple docstring'''
from string import ascii_uppercase
_lowercase = {char: i for i, char in enumerate(ascii_uppercase)}
_lowercase = dict(enumerate(ascii_uppercase))
def A (__lowerCamelCase :str , __lowerCamelCase :str ):
_lowerCAmelCase = len(__lowerCamelCase )
_... | 229 |
'''simple docstring'''
import os
def A ():
with open(os.path.dirname(__lowerCamelCase ) + """/grid.txt""" ) as f:
_lowerCAmelCase = [] # noqa: E741
for _ in range(20 ):
l.append([int(__lowerCamelCase ) for x in f.readline().split()] )
... | 229 | 1 |
'''simple docstring'''
import baseaa
def __snake_case( _lowerCAmelCase ) -> bytes:
return baseaa.baaencode(string.encode("""utf-8""" ) )
def __snake_case( _lowerCAmelCase ) -> str:
return baseaa.baadecode(_lowerCAmelCase ).decode("""utf-8""" )
if __name__ == "_... | 35 |
'''simple docstring'''
import string
from math import logaa
def __snake_case( _lowerCAmelCase , _lowerCAmelCase ) -> int:
snake_case__ : List[str] = document.translate(
str.maketrans("""""" , """""" , string.punctuation ) ).replace("""\n""" ... | 35 | 1 |
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class __snake_case ( a ):
UpperCAme... | 363 |
import os
import warnings
from typing import List, Optional
from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig
snake_case_ : Union[str, Any] = logging.get_logger(__name__)
class __snake_case :
... | 7 | 0 |
import numpy as np
import torch
from torch.utils.data import DataLoader
from accelerate.utils.dataclasses import DistributedType
class snake_case__:
'''simple docstring'''
def __init__( self , __lowercase=2 , __lowercase=3 , __lowercase=6_4 , __lowercase=None ... | 262 |
from __future__ import annotations
from typing import Dict
from ...configuration_utils import PretrainedConfig
_UpperCAmelCase : Dict ={
"""susnato/ernie-m-base_pytorch""": """https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json""",
"""susnato/ernie-m-large_pytorch""": """https... | 262 | 1 |
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis
from lavis.models import load_model_and_preprocess
from PIL import Image
from transformers import (
AutoTokenize... | 200 |
from math import sqrt
def __lowerCamelCase ( UpperCamelCase__ ):
'''simple docstring'''
snake_case_ = 0
for i in range(1 , int(sqrt(UpperCamelCase__ ) + 1 ) ):
if n % i == 0 and i != sqrt(UpperCamelCase__ ):
... | 200 | 1 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
a__ : str =logging.get_logger(__name__)... | 53 |
def _snake_case( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> List[str]:
if index == r:
for j in range(SCREAMING_SNAKE_CASE__ ):
prin... | 20 | 0 |
"""simple docstring"""
import re
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class lowerCamelCase_( A__ ):
'''simple docstring'''
lowercase__ : Tuple = ['image_processor', 'tokenizer']
lowercase__ : Di... | 73 |
"""simple docstring"""
import os
from collections.abc import Iterator
def lowerCAmelCase_( lowercase_ : str = "." ) -> Iterator[str]:
for dir_path, dir_names, filenames in os.walk(lowercase_ ):
_lowerCamelCase = [d for d in dir_names if d != '''scripts''' and... | 73 | 1 |
import argparse
import torch
from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging
logging.set_verbosity_info()
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAm... | 189 |
import argparse
import torch
from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging
logging.set_verbosity_info()
def UpperCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> List[Any]:
#... | 156 | 0 |
def __magic_name__ ( __lowerCAmelCase : int , __lowerCAmelCase : int ) -> bool:
return numa ^ numa < 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 355 |
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,
AutoTokenizer,
... | 339 | 0 |
"""simple docstring"""
import torch
from torch import nn
class __lowerCAmelCase ( nn.Module ):
'''simple docstring'''
def __init__( self , _a , _a , _a , _a , _a=1 , _a=False ):
super().__init__()
__a = n... | 45 | '''simple docstring'''
from itertools import product
def lowerCamelCase ( UpperCAmelCase__ : int , UpperCAmelCase__ : int ) -> list[int]:
lowercase_ : List[Any] = sides_number
lowercase_ : Dict = max_face_nu... | 239 | 0 |
"""simple docstring"""
import argparse
import OmegaConf
import torch
from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel
def lowercase ( _SCREAMING_SNAKE_CASE : Tuple , _SCREAMING_SNAKE_CASE : List[Any] , _SCREAMING_SNAKE_CASE : Union[... | 326 |
"""simple docstring"""
class _a :
"""simple docstring"""
def __init__( self : Tuple , __UpperCamelCase : list[int] )->None:
_UpperCAmelCase = len(__UpperCamelCase )
_UpperCAmelCase = [0] * len_array
... | 326 | 1 |
'''simple docstring'''
from ....configuration_utils import PretrainedConfig
from ....utils import logging
lowerCamelCase : Union[str, Any] = logging.get_logger(__name__)
lowerCamelCase : Union[str, Any] = {
'speechbrain/m-ctc-t-large': 'https://huggingface.co/speechbrain/m-ctc-t... | 2 |
'''simple docstring'''
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 snake_case... | 304 | 0 |
from math import ceil
def _lowerCAmelCase ( __lowerCAmelCase , __lowerCAmelCase ) -> Optional[int]:
"""simple docstring"""
snake_case__ : Any = list(range(0 , __lowerCAmelCase ) )
snake_case__ : int = [item for sublist in l... | 353 |
import os
import unittest
from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class a ( __lowerCamelCase , unittest.TestCase ):
__lowerCAmelCase : Dict ... | 44 | 0 |
'''simple docstring'''
import argparse
import torch
from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging
logging.set_verbosity_info()
def _UpperCamelCase ( UpperCamelCase__ , UpperCamelCase__... | 163 |
'''simple docstring'''
import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch
from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search
from transformers.testing_utils import CaptureStdout, TestCasePlus... | 163 | 1 |
"""simple docstring"""
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
"""RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json""",
}
class __snake_case ( SCREAMIN... | 353 |
"""simple docstring"""
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 __snake_ca... | 291 | 0 |
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartaaTokenizer, MBartaaTokenizerFast, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
... | 222 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_UpperCAmelCase : Union[str, Any] = {
"configuration_instructblip": [
"INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
"InstructBlipConfig",
"InstructBlipQFor... | 222 | 1 |
"""simple docstring"""
from __future__ import annotations
import math
def _snake_case ( lowercase__ : int ) -> list[int]:
'''simple docstring'''
if num <= 0:
lowerCAmelCase_ :Dict = f"""{num}: Invalid input, please enter a positiv... | 364 |
"""simple docstring"""
def _snake_case ( lowercase__ : int = 5_0 ) -> int:
'''simple docstring'''
lowerCAmelCase_ :int = [1] * (length + 1)
for row_length in range(3 , length + 1 ):
for block_length in range(3 , row_length + ... | 1 | 0 |
'''simple docstring'''
def snake_case_ (_a : int ):
if isinstance(_a , _a ):
raise TypeError('''\'float\' object cannot be interpreted as an integer''' )
if isinstance(_a , _a ):
raise TypeError('''\'str\' object cannot be interpreted as a... | 34 |
'''simple docstring'''
import contextlib
import importlib
import io
import unittest
import transformers
# Try to import everything from transformers to ensure every object can be loaded.
from transformers import * # noqa F406
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, requi... | 237 | 0 |
import math
import random
from typing import Any
from .hill_climbing import SearchProblem
def lowerCAmelCase__ ( _a : List[Any] , _a : bool = True , _a : float = math.inf , _a : float = -math.inf , _a : float = math.inf , _a : floa... | 367 |
import numpy as np
def lowerCAmelCase__ ( _a : np.array ):
return (2 / (1 + np.exp(-2 * vector ))) - 1
if __name__ == "__main__":
import doctest
doctest.testmod()
| 36 | 0 |
'''simple docstring'''
import importlib
import shutil
import threading
import warnings
from typing import List
import fsspec
import fsspec.asyn
from . import compression
from .hffilesystem import HfFileSystem
_A : Optional[int] = importlib.util.find_spec('''s3fs''') is not None
... | 229 | '''simple docstring'''
from unittest import TestCase
from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset
class _lowercase ( UpperCAmelCase__ ):
'''simple docstring'''
def a ( self : int ) -> Optional[Any]:
return ... | 229 | 1 |
"""simple docstring"""
import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List
from ..utils import logging
UpperCAmelCase : List[Any] = logging.get_logger(__name__)
def _SCREAMING_SNAKE_CASE (__lower... | 313 |
"""simple docstring"""
import unittest
from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
... | 313 | 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 : List[Any] = logging.get_logger(__name__)
__lowerCAmelCase : Optional[Any] ... | 156 |
from typing import Dict
from .base import GenericTensor, Pipeline
class A ( _UpperCAmelCase ):
"""simple docstring"""
def snake_case__ ( self : int,lowercase_ : Dict=None,lowercase_ : Tuple=None,lowercase_ : List[Any]=None,... | 7 | 0 |
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class lowercase__ ( _UpperCAmelCase ):
A__ : UNe... | 351 |
import argparse
import os
import transformers
from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging
logging.set_verbosity_info()
__snake_case = logging.get_logger(__name__)
__snake_case = {name: getattr(transformers, name + """Fast""") for name in SLOW_TO... | 169 | 0 |
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