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'''
import argparse
import json
from collections import OrderedDict
import torch
from huggingface_hub import cached_download, hf_hub_url
from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification
def lowercase__ ( __lowercase : Any ) -> Tuple:
... | 53 |
'''simple docstring'''
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.... | 53 | 1 |
from argparse import ArgumentParser
from . import BaseTransformersCLICommand
def UpperCamelCase (lowercase_: int ) -> str:
return DownloadCommand(args.model , args.cache_dir , args.force , args.trust_remote_code )
class _a (__magic_name__ ):
'''simple docstring'''... | 141 |
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,
EfficientFormerIma... | 141 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_o... | 272 | '''simple docstring'''
from ...utils import logging
from ..ta.modeling_tf_ta import TFTaEncoderModel, TFTaForConditionalGeneration, TFTaModel
from .configuration_mta import MTaConfig
__lowercase = logging.get_logger(__name__)
__lowercase = '''T5Config'''
class a__( lowerCAmelCas... | 272 | 1 |
'''simple docstring'''
import argparse
import json
import os
import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile
from transformers.modeling_utils import dtype_byte_size
from transformer... | 353 |
'''simple docstring'''
import logging
import os
from typing import Dict, List, Optional, Union
import torch
import torch.nn as nn
from accelerate.utils.imports import (
is_abit_bnb_available,
is_abit_bnb_available,
is_bnb_available,
)
from ..big_modeling import dispatch_model, i... | 283 | 0 |
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Optional[int] , SCREAMING_SNAKE_CASE_: int ... | 68 |
import os
from pathlib import Path
from unittest.mock import patch
import pytest
import zstandard as zstd
from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
OfflineModeIsEnabled,
cached_path,
fsspec_get,
fsspec_head,
ftp_get,
ftp_head,
ge... | 68 | 1 |
"""simple docstring"""
def _a ( _SCREAMING_SNAKE_CASE ) -> Optional[int]:
stooge(_lowerCamelCase , 0 , len(_lowerCamelCase ) - 1 )
return arr
def _a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> List[str]:
if i >= h:
... | 370 |
"""simple docstring"""
import math
class __A :
'''simple docstring'''
def __init__( self : List[str] , UpperCAmelCase_ : Tuple=0 ) ->Optional[int]: # a graph with Node 0,1,...,N-1
"""simple docstring"""
snake_case_ ... | 233 | 0 |
from __future__ import annotations
import math
import random
from collections.abc import Collection
from typing import overload
class snake_case__ :
def __init__( self , lowerCAmelCase__ = None ) -> None:
if components is None:
__magic_name__ : Any ... | 342 |
import math
class snake_case__ :
def __init__( self , lowerCAmelCase__=0 ) -> Optional[int]: # a graph with Node 0,1,...,N-1
__magic_name__ : Tuple = n
__magic_name__ : Union[str, Any] = [
[math.inf for j in range(0 , ... | 342 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_UpperCAmelCase = {
"""configuration_bridgetower""": [
"""BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""BridgeTowerConfig""",
... | 192 | import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel
from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
... | 192 | 1 |
import random
import unittest
from torch.utils.data import BatchSampler, DataLoader, IterableDataset
from accelerate import Accelerator
from accelerate.data_loader import (
BatchSamplerShard,
DataLoaderDispatcher,
DataLoaderShard,
IterableDatasetShard,
SkipBatchSampler,
... | 29 |
import math
from typing import Dict, Iterable, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
IMAG... | 235 | 0 |
'''simple docstring'''
import argparse
import os
import torch
from transformers import FlavaConfig, FlavaForPreTraining
from transformers.models.flava.convert_dalle_to_flava_codebook import convert_dalle_checkpoint
def __lowerCamelCase ( __lowerCAmelCase : Any ) ... | 363 |
'''simple docstring'''
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from huggingface_hub import HfFolder, Repository, create_repo, delete_repo
from requests.exceptions import HTTPError
import transformers
from tr... | 3 | 0 |
from .configuration_bert_masked import MaskedBertConfig
from .modeling_bert_masked import (
MaskedBertForMultipleChoice,
MaskedBertForQuestionAnswering,
MaskedBertForSequenceClassification,
MaskedBertForTokenClassification,
MaskedBertModel,
)
from .modules import *
| 19 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__A ={'''configuration_xglm''': ['''XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP'''... | 19 | 1 |
"""simple docstring"""
from __future__ import annotations
from math import pi, sqrt
def UpperCamelCase ( lowerCAmelCase__ , lowerCAmelCase__ ):
'''simple docstring'''
if inductance <= 0:
raise ValueError('''Inductance cannot be 0 or negative''' )
elif capacita... | 353 |
import os
from typing import BinaryIO, Optional, Union
import numpy as np
import pyarrow.parquet as pq
from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
from ..packaged_modules import _PACKAGED_DATASETS_M... | 97 | 0 |
'''simple docstring'''
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,
... | 3 | 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
_SCREAMING_... | 343 | 0 |
def __lowerCamelCase (UpperCAmelCase__ : int ):
assert (
isinstance(UpperCAmelCase__ , UpperCAmelCase__ ) 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
SC... | 206 | import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase : int = logging.get_logger(__name__)
_lowerCamelCase : Union[str, Any] = {
'''microsoft/unispeech-sat-base-100h-libri-ft''': (
''... | 206 | 1 |
from __future__ import annotations
def lowerCAmelCase__ ( a__: list[list[int]] ) -> int:
'''simple docstring'''
for i in range(1 , len(matrix[0] ) ):
matrix[0][i] += matrix[0][i - 1]
# preprocessing the first column
for i in range(1 ... | 329 |
"""simple docstring"""
import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type
from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iter... | 45 | 0 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
lowercase__ = logging.get_logger(__name_... | 366 |
'''simple docstring'''
from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar
lowercase__ = TypeVar("KEY")
lowercase__ = TypeVar("VAL")
@dataclass(frozen=_snake_case , slots=_snake_case )
cla... | 280 | 0 |
'''simple docstring'''
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_g... | 22 |
"""simple docstring"""
def _lowerCamelCase ( _UpperCamelCase ):
'''simple docstring'''
__lowerCAmelCase = False
while is_sorted is False: # Until all the indices are traversed keep looping
__lowerCAmelCase = True
for i in range(0 , len(_UpperCamelCase ) - ... | 57 | 0 |
"""simple docstring"""
import multiprocessing
import time
from arguments import PretokenizationArguments
from datasets import load_dataset
from transformers import AutoTokenizer, HfArgumentParser
def UpperCamelCase ( lowerCAmelCase__ ):
'''simple docstring'''
lowercase = {}
... | 371 |
import collections
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowercase__ :Dict = logging.get_logger(__name__)
lowercase__ :Optional[Any] = "▁"
lowercase__ :str = ... | 97 | 0 |
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase ) -> Union[str, Any]:
'''simple docstring'''
lowerCAmelCase : Optional[Any] = len(_UpperCAmelCase )
for i in range(length - 1 ):
lowerCAmelCase : Optional[int] = i
for k in range(i + 1, _UpperCAmel... | 138 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : List[str] = logging.get_logger(__name__)
__A : Optional[Any] = {
'''facebook/vit-mae-base''': '''https://huggingface.co/facebook/vit-mae-base/resolve/main/config.json''',
# See all ViT MAE... | 138 | 1 |
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
lowerCAmelCase__ = '''\
@misc{wu2016googles,
title={Google\'s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},
author={Yonghui Wu and ... | 121 |
from typing import Union
import fire
import torch
from tqdm import tqdm
def __lowerCamelCase ( lowerCamelCase__ , lowerCamelCase__ = "cpu" , lowerCamelCase__ = None ):
"""simple docstring"""
lowercase__ : Any = torch.load(lowerCamelCase__ , map_locat... | 121 | 1 |
'''simple docstring'''
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING:
import torch
cla... | 63 |
from collections import defaultdict
def __snake_case ( _UpperCAmelCase , _UpperCAmelCase ):
__a = first_str.lower().strip()
__a = second_str.lower().strip()
# Remove whitespace
__a = first_str.replace(''' ''' , '''''' )
__a ... | 49 | 0 |
from __future__ import annotations
def UpperCamelCase( lowercase_ ) -> bool:
'''simple docstring'''
if len(lowercase_ ) < 2:
raise ValueError("""Monogons and Digons are not polygons in the Euclidean space""" )
if any(i <= 0 for i in nums ):
raise ValueE... | 34 |
def UpperCamelCase( lowercase_ , lowercase_ ) -> str:
'''simple docstring'''
return "\n".join(
f'''{number} * {i} = {number * i}''' for i in range(1 , number_of_terms + 1 ) )
if __name__ == "__main__":
print(multiplication_table(number=5, number_of_t... | 34 | 1 |
import logging
import os
from typing import Dict, List, Optional, Union
import torch
import torch.nn as nn
from accelerate.utils.imports import (
is_abit_bnb_available,
is_abit_bnb_available,
is_bnb_available,
)
from ..big_modeling import dispatch_model, init_empty_weights
from .datac... | 32 |
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,
AutoModelForSeqaSeqL... | 24 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase_ = {'''configuration_vit_msn''': ['''VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTMSNConfig''']}
try:
if not is_torch_available():
... | 350 |
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from accelerate import Accelerator
from accelerate.t... | 59 | 0 |
import warnings
from ...utils import logging
from .image_processing_poolformer import PoolFormerImageProcessor
A__: Optional[Any] = logging.get_logger(__name__)
class _a ( UpperCamelCase__):
"""simple docstring"""
def __init__( self: Union[s... | 149 |
import doctest
from collections import deque
import numpy as np
class _a :
"""simple docstring"""
def __init__( self: Union[str, Any] ):
'''simple docstring'''
UpperCamelCase__: int = [2, 1, 2, -1]
UpperCa... | 149 | 1 |
"""simple docstring"""
from __future__ import annotations
def a__ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) -> set[str]:
__lowerCAmelCase , __lowerCAmelCase: str = set(__SCREAMING_SNAKE_CASE ), [start]
while stack:
__lowerCAmelCase: Optio... | 108 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A ... | 108 | 1 |
'''simple docstring'''
def lowercase__ ( __lowercase : int , __lowercase : int ) -> int:
"""simple docstring"""
while a != 0:
__UpperCamelCase , __UpperCamelCase = b % a, a
return b
def lowercase__ ( __lowerca... | 53 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel... | 331 | 0 |
"""simple docstring"""
def A ( snake_case :int ) -> int:
__UpperCamelCase = abs(snake_case )
__UpperCamelCase = 0
while n > 0:
res += n % 1_0
n //= 1_0
return res
def A ( snake_case :int ) -> int:
__UpperCamelCase = abs(snake_case )
... | 263 |
"""simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase : str = logging.get_logger(__name__)
UpperCamelCase : Dict = {
"microsoft/wavlm-base": "https://huggingface.co/microsoft/wavlm-b... | 263 | 1 |
import math
def _UpperCAmelCase ( SCREAMING_SNAKE_CASE__ : int ):
if not isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
__UpperCamelCase =F'Input value of [number={number}] must be an integer'
raise TypeError(SCREAMING_SNAKE_CASE__ )... | 62 |
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
_A = logging.get_logger(__name__)
_A = '▁'
_A = {'vocab_fil... | 62 | 1 |
"""simple docstring"""
import csv
import tweepy
# Twitter API credentials
__A = ''''''
__A = ''''''
__A = ''''''
__A = ''''''
def lowercase_ ( _lowerCamelCase: str ) -> None:
'''simple docstring'''
__lowerCamelCase : str = tweepy.OAuthH... | 64 | """simple docstring"""
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
torch.set_grad... | 64 | 1 |
"""simple docstring"""
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from huggingface_hub import HfFolder, Repository, create_repo, delete_repo
from requests.exceptions import HTTPError
import transformers
from transforme... | 148 |
import ast
import os
import re
import shutil
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.test_utils.examples import compare_against_test
from accelerate.test_utils.testing import TempDirTestCase, require_trackers, run_command, slow
from accelerate.utils import write_bas... | 169 | 0 |
'''simple docstring'''
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,
Stable... | 17 | '''simple docstring'''
from math import factorial
def __UpperCAmelCase ( a_: int = 100 ):
return sum(map(a_, str(factorial(a_ ) ) ) )
if __name__ == "__main__":
print(solution(int(input('Enter the Number: ').strip()))) | 17 | 1 |
"""simple docstring"""
import tempfile
import unittest
import numpy as np
from diffusers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionPipeline,
PNDMScheduler,
)
from diffuse... | 293 |
"""simple docstring"""
def __A (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->float:
"""simple docstring"""
if discount_rate < 0:
raise ValueError('Discount rate cannot be negative' )
if not cash_flows:
raise ValueError('Cash flows list cannot be empty' )
lowerCA... | 293 | 1 |
"""simple docstring"""
class lowerCamelCase :
'''simple docstring'''
def __init__(self , _lowerCamelCase ):
"""simple docstring"""
UpperCAmelCase__ : Union[str, Any] = len(_lowerCamelCase )
UpperCAmelCase__ : Union[str, Any] = [0] *... | 166 |
"""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 lowerCamelCase ( lowerCAmelCase__ ):
... | 166 | 1 |
import os
from collections.abc import Iterator
def A_ ( A__ = "." ) -> Iterator[str]:
for dir_path, dir_names, filenames in os.walk(A__ ):
a__ : Dict = [d for d in dir_names if d != 'scripts' and d[0] not in '._']
for filename in filenames:
... | 99 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase : List[Any] = {
"""configuration_nllb_moe""": [
"""NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""NllbMoeConfig""",
]
}
try:
if not is_... | 99 | 1 |
"""simple docstring"""
import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel
from ...models.attention import BasicTransformerBlock
from ...utils import logging
SCREAMING_SNAKE_CASE_ : int = logging.get_logger(__name__) # pylint: disable=inv... | 69 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_owlvit import OwlViTImageProcessor
SCREAMING_SNAKE_CASE_ : Tuple = logging.get_logger(__name__)
class a ( _lowerCamelCase ):
"""simple docstring"""
def __in... | 69 | 1 |
'''simple docstring'''
def _UpperCamelCase ( __A , __A , __A ) -> List[str]:
'''simple docstring'''
if n == 0:
return 1
elif n % 2 == 1:
return (binary_exponentiation(__A , n - 1 , __A ) * a) % mod
else:
... | 80 |
"""simple docstring"""
import torch
from diffusers import StableDiffusionPipeline
__A = "path-to-your-trained-model"
__A = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to("cuda")
__A = "A photo of sks dog in a bucket"
__A ... | 148 | 0 |
import argparse
import re
import requests
import torch
# git clone https://github.com/salesforce/BLIP.git
from models.blip import blip_decoder
from models.blip_itm import blip_itm
from models.blip_vqa import blip_vqa
from PIL import Image
from torchvision import transforms
from torchvision.transforms.functional impo... | 47 |
import json
import os
import shutil
import warnings
from argparse import ArgumentParser, Namespace
from pathlib import Path
from typing import List
from ..utils import logging
from . import BaseTransformersCLICommand
try:
from cookiecutter.main import cookiecutter
__A =True
except ImportError:
__A =... | 47 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__lowerCAmelCase : str = logging.get_logger(__name__)
__lowerCAmelCase : Tuple = {
'roberta-base': 'http... | 107 |
'''simple docstring'''
import math
def a__ ( lowercase : list, lowercase : int = 0, lowercase : int = 0 ) -> list:
"""simple docstring"""
_UpperCamelCase = end or len(lowercase )
for i in range(lowercase, lowercas... | 324 | 0 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, lo... | 356 |
import multiprocessing
from typing import TYPE_CHECKING, Optional, Union
from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream
if TYPE_CHECKING:
import sqlitea
import ... | 118 | 0 |
from unittest.mock import Mock, patch
from file_transfer.send_file import send_file
@patch('''socket.socket''' )
@patch('''builtins.open''' )
def UpperCAmelCase_ ( __snake_case , __snake_case ) -> List[Any]:
"""simple docstring"""
_lowercase =Mock()... | 5 |
from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar
SCREAMING_SNAKE_CASE_:List[Any] = TypeVar("""KEY""")
SCREAMING_SNAKE_CASE_:Dict = TypeVar("""VAL""")
@dataclass(frozen=SCREAMING_SNAKE_CASE__ , slo... | 116 | 0 |
from ..utils import is_flax_available, is_torch_available
if is_torch_available():
from .autoencoder_kl import AutoencoderKL
from .controlnet import ControlNetModel
from .dual_transformer_ad import DualTransformeraDModel
from .modeling_utils import ModelMixin
from .prior_transformer import PriorTransformer
... | 353 |
from typing import Any, Dict, List, Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available(... | 131 | 0 |
"""simple docstring"""
import warnings
from .generation import TFGenerationMixin
class lowerCAmelCase__ ( UpperCAmelCase__ ):
'''simple docstring'''
warnings.warn(
"Importing `TFGenerationMixin` from `src/transformers/generation_tf_utils.py` is deprecated and w... | 91 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowercase__ = logging.get_logger(__name__)
lowercase__ =... | 151 | 0 |
import collections
import inspect
import unittest
from transformers import FocalNetConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import Backbon... | 258 | import unicodedata
from dataclasses import dataclass
from typing import Optional, Union
import numpy as np
from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase
def A (... | 258 | 1 |
"""simple docstring"""
from collections import deque
def _SCREAMING_SNAKE_CASE ( lowercase_ ) -> List[str]:
A__ = len(lowercase_ )
A__ = deque()
A__ = [False for _ in range(lowercase_ )]
A__ = [-1 for _ in range(lowercase_ )]
A_... | 247 |
"""simple docstring"""
import os
from pathlib import Path
def _SCREAMING_SNAKE_CASE ( ) -> Tuple:
from torch.utils.cpp_extension import load
A__ = Path(lowercase_ ).resolve().parent.parent.parent / "kernels" / "deformable_detr"
A__ = [
root / filename
... | 247 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
'EleutherAI/gpt-neox-20b': 'https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json',
# See all GPTNeoX models at https... | 360 |
"""simple docstring"""
import argparse
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt
if __name__ == "__main__":
__A = argparse.ArgumentParser()
parser.add_argument(
'--checkpoint_path', default=None, type=str, required=Tr... | 341 | 0 |
import argparse
import math
import os
from copy import deepcopy
import torch
from audio_diffusion.models import DiffusionAttnUnetaD
from diffusion import sampling
from torch import nn
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
_lowercase : Tuple ={
... | 170 |
'''simple docstring'''
import unittest
from dataclasses import dataclass
import pytest
from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict
@dataclass
class _snake_... | 139 | 0 |
import unittest
from transformers import SPIECE_UNDERLINE
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.tokenization_utils import AddedToken
from ...test_tokenization_common import Tok... | 223 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase_ : Optional[int] = logging.get_logger(__name__)
lowerCamelCase_ : Dict = {
"""studio-ousia/luke-base""": """https://huggingface.co/studio-ousia/luke-base/resolve/main/config.json""",
"""studio-o... | 223 | 1 |
from collections import Counter
from pathlib import Path
from typing import Optional, Tuple
import yaml
class __snake_case ( yaml.SafeLoader ):
def UpperCAmelCase__ ( self : Dict , A_ : Tuple):
lowerCAmelCase_ : Tuple = [se... | 103 |
from datetime import datetime as dt
import os
from github import Github
A__ : List[str] = [
'''good first issue''',
'''good second issue''',
'''good difficult issue''',
'''feature request''',
'''new model''',
'''wip''',
]
def UpperCamelCase( ):
lowerCAmelCase_ : ... | 103 | 1 |
'''simple docstring'''
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_ = {
'bert-base-uncased': 'https://huggingfa... | 222 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ = {
'configuration_megatron_bert': ['MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MegatronBertConfig'],
}
try:
if not is_torch_availabl... | 222 | 1 |
"""simple docstring"""
from __future__ import annotations
class UpperCamelCase__:
def __init__( self ,__UpperCAmelCase ) -> None:
A__ = order
# a_{0} ... a_{k}
A__ = [1.0] + [0.0] * order
# b_{0} ...... | 221 | """simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCamelCase = logging.get_logger(__name__)
__lowerCamelCase = {
"transfo-xl-wt103": "https://huggingface.co/transfo-xl-wt103/resolve/main/config.json",
}
class U... | 221 | 1 |
"""simple docstring"""
lowerCAmelCase_ : Union[str, Any] = {
'''joule''': 1.0,
'''kilojoule''': 1_0_0_0,
'''megajoule''': 1_0_0_0_0_0_0,
'''gigajoule''': 1_0_0_0_0_0_0_0_0_0,
'''wattsecond''': 1.0,
'''watthour''': 3_6_0_0,
'''kilowatthour''': 3_6_0_0_0_0_0,
... | 358 |
"""simple docstring"""
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE... | 248 | 0 |
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextModel,
CLIPTokenizer,
WhisperForConditionalGeneration,
WhisperProcessor,
)
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
... | 277 |
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
a_ :Any = "\\n@misc{wu2016googles,\n title={Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},\n author={Yonghui Wu and Mi... | 277 | 1 |
'''simple docstring'''
from queue import PriorityQueue
from typing import Any
import numpy as np
def _a ( _lowercase : dict , _lowercase : str , _lowercase : set , _lowercase : set , _lowercase : dict , _lowercase : dict , _lo... | 354 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__UpperCAmelCase :List[Any] = {
"configuration_lxmert": ["LXMER... | 240 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase_ = {
'configuration_timesformer': ['TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TimesformerConfig'],
}
try:
if not is_torch_available(... | 346 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowercase_ = {"""configuration_mbart""": ["... | 58 | 0 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimen... | 371 |
def lowerCamelCase ( a_ , a_ ) -> List[Any]:
lowerCAmelCase_ = 0
while b > 0:
if b & 1:
res += a
a += a
b >>= 1
return res
def lowerCamelCase ( a_ , a... | 14 | 0 |
"""simple docstring"""
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install... | 194 |
"""simple docstring"""
from graphs.minimum_spanning_tree_kruskal import kruskal
def lowerCamelCase__ ( ) -> List[Any]:
"""simple docstring"""
_UpperCamelCase = 9
_UpperCamelCase = [
[0, 1, 4],
[0, 7, 8],
[1... | 194 | 1 |
"""simple docstring"""
import functools
from typing import Any
def lowercase_ ( _lowerCamelCase: str , _lowerCamelCase: list[str] ) -> bool:
'''simple docstring'''
if not isinstance(_lowerCamelCase , _lowerCamelCase ) or len(_lowerCamelCase ) == 0:
raise ValueE... | 64 | """simple docstring"""
def lowercase_ ( _lowerCamelCase: int = 100 ) -> int:
'''simple docstring'''
__lowerCamelCase : Optional[Any] = set()
__lowerCamelCase : Union[str, Any] = 0
__lowerCamelCase : Optional[Any] ... | 64 | 1 |
"""simple docstring"""
import importlib.metadata
import warnings
from copy import deepcopy
from packaging import version
from ..utils import logging
from .import_utils import is_accelerate_available, is_bitsandbytes_available
if is_bitsandbytes_available():
import bitsandbytes as bnb
import torch
import... | 102 |
"""simple docstring"""
import logging
import math
import os
from dataclasses import dataclass, field
from glob import glob
from typing import Optional
from torch.utils.data import ConcatDataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_WITH_LM_HEAD_MAPPING,
AutoConfig,
Au... | 102 | 1 |
import mpmath # for roots of unity
import numpy as np
class __A :
def __init__( self : Optional[int] , UpperCAmelCase_ : Dict=None , UpperCAmelCase_ : str=None ):
# Input as list
lowerCAmelCase : str = list(poly_a or... | 357 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
i... | 323 | 0 |
'''simple docstring'''
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class A__ ( UpperCAmelCase__ ):
@staticmethod
@abstractmethod
def __UpperCAmelCase ( SCREAMING_SNAKE_CASE :ArgumentParser ) -... | 276 |
'''simple docstring'''
A__: Tuple = '''
# Installazione di Transformers
! pip install transformers datasets
# Per installare dalla fonte invece dell\'ultima versione rilasciata, commenta il comando sopra e
# rimuovi la modalità commento al comando seguente.
# ! pip install git+http... | 276 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
UpperCAmelCase : Dict = {"""configuration_encoder_decoder""": ["""EncoderDecoderConfig"""]}
try:
... | 365 |
import inspect
import unittest
from transformers import MobileNetVaConfig
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_configuration_common import ConfigT... | 148 | 0 |
import os
from datetime import datetime as dt
from github import Github
UpperCAmelCase : str = [
"""good first issue""",
"""good second issue""",
"""good difficult issue""",
"""enhancement""",
"""new pipeline/model""",
"""new scheduler""",
"""wip""",
... | 95 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available... | 95 | 1 |
'''simple docstring'''
from typing import List, Optional, Union
import numpy as np
import tensorflow as tf
from .utils import logging
a_ : List[Any] = logging.get_logger(__name__)
def _A (lowerCAmelCase__ :Union[tf.Tensor, np.ndarray] ) -> ... | 371 |
'''simple docstring'''
from __future__ import annotations
from sys import maxsize
from typing import Generic, TypeVar
a_ : Optional[Any] = TypeVar("T")
def _A (lowerCAmelCase__ :int ) -> int:
'''simple docstring'''
r... | 104 | 0 |
import argparse
import struct
import unittest
class SCREAMING_SNAKE_CASE__ :
def __init__( self : Any , SCREAMING_SNAKE_CASE__ : bytes ) -> None:
a_ : Tuple = data
# Initialize hash values
a_ : Tuple ... | 32 |
from jiwer import compute_measures
import datasets
__snake_case : Dict ='\\n@inproceedings{inproceedings,\n author = {Morris, Andrew and Maier, Viktoria and Green, Phil},\n year = {2004},\n month = {01},\n pages = {},\n title = {From WER and RIL to MER and WIL: improved evaluation mea... | 129 | 0 |
'''simple docstring'''
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: F4... | 353 | '''simple docstring'''
import torch
from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde
from .test_schedulers import SchedulerCommonTest
@require_torchsde
class a__ ( UpperCAmelCase__ ):
lowerCamelCa... | 237 | 0 |
'''simple docstring'''
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ : str = logging.get_logger(__name__)
class snake_case ( lowercase ):
"""simple docstring"""
_lowerCamelCase = "encoder-d... | 55 |
import inspect
import unittest
import warnings
from math import ceil, floor
from transformers import LevitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vi... | 195 | 0 |
"""simple docstring"""
from __future__ import annotations
from fractions import Fraction
def SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase , __UpperCAmelCase ) -> bool:
return (
num != den and num % 1_0 == den // 1_0 and (num // 1_0) / (den % 1_0) == num / den
)
def SCREAMI... | 361 | """simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
"micro... | 2 | 0 |
from __future__ import annotations
from random import choice
def _a ( SCREAMING_SNAKE_CASE_ : List[str] ):
return choice(SCREAMING_SNAKE_CASE_ )
def _a ( SCREAMING_SNAKE_CASE_ : list[int] , SCREAMING_SNAKE_CASE_ : int ):
... | 92 | """simple docstring"""
from __future__ import annotations
import numpy as np
def UpperCAmelCase__ ( lowerCAmelCase__ :list[float] ) -> Optional[Any]:
'''simple docstring'''
return np.maximum(0 , lowerCAmelCase__ )
if __name__ == "__main__":
p... | 197 | 0 |
"""simple docstring"""
import math
class UpperCamelCase :
def _UpperCAmelCase ( self ,__UpperCamelCase ,__UpperCamelCase ) -> int:
'''simple docstring'''
lowercase_ : Tuple = 0.0
lowercase_ : Optional[Any] ... | 321 | """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_modelin... | 321 | 1 |
import argparse
import json
import re
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileNetVaConfig,
MobileNetVaForImageClassification,
MobileNetVaImageProcessor,
load_tf_weight... | 147 |
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
lowerCAmelCase : Optional[Any] = logging.get_logger(__name__)
lowerCAmelCase : Any = ... | 253 | 0 |
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, DDPMScheduler, Stab... | 306 |
import torch
from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde
from .test_schedulers import SchedulerCommonTest
@require_torchsde
class lowerCAmelCase ( __a ):
'''simple docstring'''
_A :... | 306 | 1 |
"""simple docstring"""
import unittest
from knapsack import knapsack as k
class lowerCAmelCase_ ( unittest.TestCase ):
"""simple docstring"""
def snake_case ( self ):
"""simple docstring"""
snake_case = 0
snake_case ... | 150 | """simple docstring"""
from arguments import InitializationArguments
from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser
# Configuration
SCREAMING_SNAKE_CASE__ = HfArgumentParser(InitializationArguments)
SCREAMING_SNAKE_CASE__ = parser.parse_args()
... | 150 | 1 |
import os
from pickle import UnpicklingError
from typing import Dict, Tuple
import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict, unflatten_dict
import transformers
from .utils import logging
_lowerCAmelCase : str = ... | 367 |
import flax.linen as nn
import jax.numpy as jnp
from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD
class __magic_name__ ( nn.Module ):
SCREAMING_SNAKE_CASE = 42
SCREAMING_SNAKE_CAS... | 308 | 0 |
"""simple docstring"""
def SCREAMING_SNAKE_CASE ( _lowerCamelCase : List[Any] ,_lowerCamelCase : Optional[Any] ,_lowerCamelCase : List[Any]=False ) -> str:
if isinstance(__lowerCAmelCase ,__lowerCAmelCase ) and isinstance(__lowerCAmelCase ,__lowerC... | 44 |
A__ = [4, 1, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
A__ = [3, 7, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
A__ = {
0: '''Sunday''',
1: '''Monday''',
2: '''Tuesday''',
3: '''Wednesday''',
4: '''Thursday''',
5: '''Friday''',
6: '''Saturday''',
}
def _... | 230 | 0 |
'''simple docstring'''
from typing import Dict, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import flip_channel_order, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
Chan... | 142 |
'''simple docstring'''
import pprint
import requests
UpperCamelCase_ : Tuple = '''https://zenquotes.io/api'''
def __a ( ) -> list:
"""simple docstring"""
return requests.get(API_ENDPOINT_URL + "/today" ).json()
def __a ( ) ... | 142 | 1 |
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
fr... | 147 |
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, ids_tensor, ra... | 147 | 1 |
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import... | 211 |
import math
class _a :
def __init__( self : List[Any] , _SCREAMING_SNAKE_CASE : Any=0 )-> Optional[Any]: # a graph with Node 0,1,...,N-1
lowerCAmelCase__ : Optional[int] = n
lowerCAmelCase__ : List[Any] = [
[math.inf fo... | 211 | 1 |
import qiskit
def snake_case ( snake_case__ :int , snake_case__ :int) -> qiskit.result.counts.Counts:
_A = qiskit.Aer.get_backend("""aer_simulator""")
# Create a Quantum Circuit acting on the q register
_A = qiskit.QuantumCircuit(snake_case__ ... | 180 | def snake_case ( snake_case__ :str = "The quick brown fox jumps over the lazy dog" , ) -> bool:
_A = set()
# Replace all the whitespace in our sentence
_A = input_str.replace(""" """ , """""")
for alpha in input_str:
if "a" <= alpha.l... | 180 | 1 |
def _lowercase ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ) -> bool:
return not any(
neighbour == 1 and colored_vertices[i] == color
for i, neighbour in enumerate(_UpperCAmelCase ) )
def _lowercase ( _UpperCAmelCase , _UpperCAmelCase ... | 262 |
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,... | 262 | 1 |
import math
def lowerCAmelCase_ ( __lowerCAmelCase )-> bool:
'''simple docstring'''
assert isinstance(_A , _A ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2 and 3 are primes
return... | 348 |
import inspect
import unittest
from transformers import YolosConfig
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_configuration_common import ConfigTester
from ..... | 245 | 0 |
from argparse import ArgumentParser, Namespace
from ..utils import logging
from . import BaseTransformersCLICommand
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase ) -> str:
return ConvertCommand(
args.model_type , args.tf_checkpoint , args.pytorch_dump_output , ... | 45 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_UpperCAmelCase : List[Any] = {
"""configuration_trajectory_transformer""": [
"""TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""TrajectoryTran... | 45 | 1 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer
_UpperCAmelCase : List[Any] = logging.get_logger(__name__)
_... | 222 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase : Union[str, Any] = logging.get_logger(__name__)
_UpperCAmelCase : List[str] = {
"EleutherAI/gpt-neox-20b": "https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json",
... | 222 | 1 |
"""simple docstring"""
a :Optional[Any] = "0.18.2"
from .configuration_utils import ConfigMixin
from .utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_inflect_available,
is_invisible_watermark_available,
is_k_diffusion_available,
is_k_diffusion_version,
is_lib... | 56 |
"""simple docstring"""
import logging
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import librosa
import torch
from datasets import DatasetDict, load_dataset
from packaging import version
from torch import nn
from transformers import (
HfArgumentParse... | 56 | 1 |
import numpy as np
def __lowercase ( lowerCamelCase : Union[str, Any] , lowerCamelCase : Tuple , lowerCamelCase : Dict , lowerCamelCase : str , lowerCamelCase : List[Any] ):
UpperCamelCase_ : Optional[int] = int(np.ceil((x_end - xa) / h ) )
... | 175 | import argparse
import torch
from transformers import GPTaLMHeadModel, RobertaForMaskedLM
if __name__ == "__main__":
a_ = argparse.ArgumentParser(
description=(
'Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transfer Learned'
' Distillatio... | 175 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
__UpperCAmelCase = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCa... | 363 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transformers import BitConfig, Bit... | 257 | 0 |
"""simple docstring"""
import inspect
import unittest
from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, t... | 224 |
"""simple docstring"""
import argparse
import logging
import os
import datasets
import tensorflow as tf
from transformers import AutoTokenizer
lowercase__ : List[Any] = logging.getLogger(__name__)
def UpperCamelCase_ ( ) -> Dict:
"""simple docst... | 224 | 1 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType
snak... | 41 |
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_available, is_vision_available
from ... | 41 | 1 |
'''simple docstring'''
import argparse
from tax import checkpoints
from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM
def __snake_case( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> List[Any]:
snake_case__ : List[str] = AutoConfig.fro... | 35 |
'''simple docstring'''
from __future__ import annotations
def snake_case_ ( _lowerCAmelCase : list[int | float] , _lowerCAmelCase : int , _lowerCAmelCase : int ) -> int | float:
if len(_lowerCAmelCase ) == 0:
raise ValueE... | 23 | 0 |
"""simple docstring"""
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#... | 202 |
"""simple docstring"""
import inspect
import unittest
from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...... | 202 | 1 |
'''simple docstring'''
UpperCamelCase__ = {
'''km/h''': 1.0,
'''m/s''': 3.6,
'''mph''': 1.609_344,
'''knot''': 1.852,
}
UpperCamelCase__ = {
'''km/h''': 1.0,
'''m/s''': 0.277_777_778,
'''mph''': 0.621_371_192,
'''knot''': 0.539_956_803,
}
... | 181 |
import colorsys
from PIL import Image # type: ignore
def __UpperCamelCase ( lowerCAmelCase__ : float , lowerCAmelCase__ : float , lowerCAmelCase__ : int ):
__a : Any = x
__a : List[Any] = y
for step in range(lowerCAmelCase__ ): # noqa: B007
__a : ... | 216 | 0 |
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class UpperCamelCase__ ( __SCREAMING_SNAKE_CASE ):
"""simple docstring"""
UpperCAmelCase_ ="Speech2TextFeatureExtractor"
UpperCAmelCase_ ="Speech... | 257 |
__UpperCAmelCase = "\n# Transformers 설치 방법\n! pip install transformers datasets\n# 마지막 릴리스 대신 소스에서 설치하려면, 위 명령을 주석으로 바꾸고 아래 명령을 해제하세요.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"
__UpperCAmelCase = [{"type": "code", "content": INSTALL_CONTENT}]
__UpperCAmelCase = {
... | 257 | 1 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_... | 204 |
def _SCREAMING_SNAKE_CASE ( lowercase : int = 10_00 ):
'''simple docstring'''
lowerCamelCase_ = 2**power
lowerCamelCase_ = 0
while n:
lowerCamelCase_ , lowerCamelCase_ = r + n % 10, n // 10
return ... | 204 | 1 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps
from ..pip... | 369 |
_snake_case : 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)
_snake_case : List[Any] = ... | 207 | 0 |
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 (
is_pipeline_test,
neste... | 15 |
# 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.version import Version
SCREAMING_SNAKE_CASE... | 15 | 1 |
import heapq as hq
import math
from collections.abc import Iterator
class UpperCAmelCase_ :
'''simple docstring'''
def __init__( self , __SCREAMING_SNAKE_CASE ):
"""simple docstring"""
UpperCamelCase : List[Any]... | 315 |
import warnings
from ...utils import logging
from .image_processing_imagegpt import ImageGPTImageProcessor
__UpperCAmelCase : Optional[int] = logging.get_logger(__name__)
class UpperCAmelCase_ ( _a):
'''simple docstring'''
def __init__( self , ... | 315 | 1 |
from collections import UserDict
from typing import Union
import numpy as np
import requests
from ..utils import (
add_end_docstrings,
logging,
)
from .audio_classification import ffmpeg_read
from .base import PIPELINE_INIT_ARGS, Pipeline
lowercase : Optional[Any] = logging.get_logger(_... | 20 |
"""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
if is... | 54 | 0 |
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelerate_available,
is_acc... | 62 |
from string import ascii_lowercase, ascii_uppercase
def SCREAMING_SNAKE_CASE_ ( __magic_name__ : str ) -> str:
"""simple docstring"""
if not sentence:
return ""
UpperCamelCase :str = dict(zip(__magic_name__ , __magic_name__ ) )
return lower_to_u... | 62 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase : List[Any] = {
"""configuration_timesformer""": ["""TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TimesformerConfig"""],
}
try:
if not is_torch_availa... | 99 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowercase_ = {
"""configuration_electra""": ["""ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP"... | 303 | 0 |
import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image
from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_process... | 257 |
from typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, FlaxT... | 257 | 1 |
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