code stringlengths 82 53.2k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
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_trust_remote_code
from ...f... | 60 |
'''simple docstring'''
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version(""">=""", """4.25.0""")):
raise OptionalDependen... | 507 | 0 |
"""simple docstring"""
import argparse
import os
import transformers
from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging
logging.set_verbosity_info()
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {name: getattr(transforme... | 719 |
"""simple docstring"""
from .glue import glue_convert_examples_to_features, glue_output_modes, glue_processors, glue_tasks_num_labels
from .squad import SquadExample, SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
from .utils import DataProcessor, InputExample, InputFeat... | 681 | 0 |
import inspect
import jax
import jax.lax as lax
import jax.numpy as jnp
from ..utils import add_start_docstrings
from ..utils.logging import get_logger
lowerCamelCase_ = get_logger(__name__)
lowerCamelCase_ = r"\n Args:\n input_ids (`jnp.ndarray` of shape `(batch_size, sequence_length)`... | 151 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowerCamelCase_ = {
"configuration_rag": ["RagConfig"],
"retrieval_rag": ["RagRetriever"],
"tokenization_rag": ["RagTokenizer"],
}
try:
if not is_torch_av... | 151 | 1 |
from __future__ import annotations
def __UpperCamelCase ( _lowerCAmelCase ) -> list[int]:
"""simple docstring"""
return [ord(_lowerCAmelCase ) - 96 for elem in plain]
def __UpperCamelCase ( _lowerCAmelCase ) -> str:
"""simple docstring"""
retur... | 520 |
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,
EfficientFormerI... | 520 | 1 |
'''simple docstring'''
import copy
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 ..auto import CONFIG_MAPPING
_lowerCAmelCase : int = ... | 261 |
'''simple docstring'''
import numpy as np
def _A ( snake_case__ : np.ndarray , snake_case__ : np.ndarray , snake_case__ : float = 1E-12 , snake_case__ : int = 1_00 , ):
assert np.shape(snake_case__ )[0] == np.shape(snake_case__ )[1]
... | 261 | 1 |
'''simple docstring'''
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 .t... | 162 |
'''simple docstring'''
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.testing_utils import require... | 162 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
UpperCamelCase_ = {
"configuration_bloom": ["BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP", "BloomConfig", "BloomOnnxConfig"],... | 28 |
snake_case__ = """ABCDEFGHIJKLMNOPQRSTUVWXYZ"""
def lowerCamelCase_ ( ):
lowercase : Optional[Any] = input('''Enter message: ''' )
lowercase : Optional[Any] = input('''Enter key [alphanumeric]: ''' )
lowercase : Uni... | 583 | 0 |
"""simple docstring"""
import itertools
import os
from collections import Counter, defaultdict
from concurrent.futures import ThreadPoolExecutor, as_completed
import numpy as np
import datasets
from .execute import check_correctness
_a : Optional[Any]= "\\n@misc{chen2021evaluating,\... | 192 | """simple docstring"""
import unittest
from queue import Empty
from threading import Thread
from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device
from ..test_modeling_common... | 192 | 1 |
"""simple docstring"""
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin,... | 482 |
"""simple docstring"""
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging... | 482 | 1 |
'''simple docstring'''
import os
import zipfile
import pytest
from datasets.utils.extract import (
BzipaExtractor,
Extractor,
GzipExtractor,
LzaExtractor,
SevenZipExtractor,
TarExtractor,
XzExtractor,
ZipExtractor,
ZstdExtractor,
)
from .utils import require_... | 654 | '''simple docstring'''
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."
)
| 654 | 1 |
import argparse
from pathlib import Path
import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version
from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging
if version.parse(f... | 282 |
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import flax
import jax.numpy as jnp
from jax import random
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedulerMixin
@flax.struct.datacla... | 282 | 1 |
import warnings
from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor
__a : str = logging.get_logger(__name__)
class A ( lowerCamelCase_ ):
def __init__( self : int , *__UpperCAme... | 559 |
import argparse
from pathlib import Path
import torch
from transformers import OPTConfig, OPTModel
from transformers.utils import logging
logging.set_verbosity_info()
__a : Union[str, Any] = logging.get_logger(__name__)
def a_ ( __snake_case ) -> str:
'''... | 559 | 1 |
from __future__ import annotations
import unittest
from transformers import BlenderbotConfig, BlenderbotTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import Co... | 304 |
import gc
import importlib.metadata
import tempfile
import unittest
from packaging import version
from transformers import (
AutoModel,
AutoModelForCausalLM,
AutoModelForSeqaSeqLM,
AutoModelForSequenceClassification,
AutoTokenizer,
BitsAndBytesConfig,
pipeline,
)... | 304 | 1 |
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import require_keras_nlp, require_t... | 713 |
import argparse
import json
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import ... | 678 | 0 |
'''simple docstring'''
from __future__ import annotations
def lowerCamelCase__ ( a , a , a ):
if len(a ) == 0:
raise ValueError('find_max() arg is an empty sequence' )
if (
left >= len(a )
or left < -len(a )
or r... | 356 |
'''simple docstring'''
import argparse
import json
import os
import re
import shutil
import torch
from transformers import BioGptConfig, BioGptForCausalLM
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from tra... | 356 | 1 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
if is_torch_available():
import torch
from transformers import XLMRobertaModel
@require_sentencepiece
@require_to... | 720 |
from __future__ import annotations
import inspect
import unittest
from typing import List, Tuple
from transformers import RegNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
fro... | 221 | 0 |
'''simple docstring'''
import collections
import json
import math
import os
import re
import time
from fnmatch import fnmatch
from typing import Dict
import requests
from slack_sdk import WebClient
_a : int = WebClient(token=os.environ["CI_SLACK_BOT_TOKEN"])
def _a (lowercase__ : ... | 56 |
'''simple docstring'''
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase_ = {
'configuration_autoformer': [
'AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'Au... | 603 | 0 |
"""simple docstring"""
import functools
def __A ( a_ :str , a_ :str) -> int:
__a : Any = len(a_)
__a : Any = len(a_)
@functools.cache
def min_distance(a_ :int , a_ :int) -> int:
# if... | 101 |
"""simple docstring"""
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=_UpperCamelCase )
class __lowercase ( _UpperCamelCase ):
... | 101 | 1 |
'''simple docstring'''
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0... | 582 |
'''simple docstring'''
import itertools
import random
import unittest
import numpy as np
from transformers import BatchFeature, SpeechTaFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extracti... | 582 | 1 |
import os
import re
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
__magic_name__ : List[str] = logging.get_log... | 410 |
from __future__ import annotations
import math
import random
from typing import Any
class A__ :
'''simple docstring'''
def __init__( self : str ):
"""simple docstring"""
UpperCamelCase = []
... | 410 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
__lowerCAmelCase = {
"configuration_perceiver": ["PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP", "... | 684 |
import fire
from utils import calculate_rouge, save_json
def __SCREAMING_SNAKE_CASE ( lowerCAmelCase: int , lowerCAmelCase: Optional[int] , lowerCAmelCase: List[Any]=None , **lowerCAmelCase: Tuple ) -> Union[str, Any]:
_UpperCAmelCase : Tuple = [x.strip() for x in open(lo... | 300 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from diffusers import (
DDIMScheduler,
KandinskyVaaControlnetPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, to... | 208 |
import gc
import unittest
from parameterized import parameterized
from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow
if is_flax_available():
import jax
import jax.numpy as jn... | 208 | 1 |
'''simple docstring'''
def lowerCamelCase__ ( a__ , a__ = 0) -> List[Any]:
"""simple docstring"""
_snake_case : List[str] = length or len(SCREAMING_SNAKE_CASE__)
_snake_case : List[Any] = False
for i in range(length - 1):
if list_... | 517 |
'''simple docstring'''
from __future__ import annotations
from math import pi
from typing import Protocol
import matplotlib.pyplot as plt
import numpy as np
class lowerCAmelCase ( lowerCamelCase__ ):
"""simple docstring"""
def __magic... | 597 | 0 |
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
logging.set_verbosity_i... | 166 |
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,
i... | 166 | 1 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicab... | 84 |
import math
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE ):
lowercase = [True] * n
lowercase = False
lowercase = False
lowercase = True
for i in range(3 , int(n**0.5 + 1 ) , 2 ):
lowercase = i * 2
while index < n:
l... | 84 | 1 |
"""simple docstring"""
# This model implementation is heavily inspired by https://github.com/haofanwang/ControlNet-for-Diffusers/
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
... | 719 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase__ : Tuple = {
"configuration_mctct": ["MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MCTCTConfig"],
"feature_extraction_mctct": ["MCTC... | 18 | 0 |
from importlib import import_module
from .logging import get_logger
__a = get_logger(__name__)
class lowercase__:
"""simple docstring"""
def __init__( self : List[str] , SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : int=None ) -> T... | 97 |
'''simple docstring'''
import functools
import gc
import inspect
import torch
from .imports import is_npu_available, is_xpu_available
def __UpperCamelCase ( *lowercase_ : Union[str, Any] ):
"""simple docstring"""
if not isinstance(lowercase_ , lowercas... | 536 | 0 |
'''simple docstring'''
from typing import Dict, Iterable, 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,
... | 712 | '''simple docstring'''
def UpperCamelCase__ ( a__ ):
'''simple docstring'''
_lowerCAmelCase =set()
# To detect a back edge, keep track of vertices currently in the recursion stack
_lowerCAmelCase =set()
return any(
node not in visited an... | 58 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import List, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCAmelCase__ : Dict = logging.get_log... | 223 |
"""simple docstring"""
import argparse
import glob
import logging
import os
import time
from argparse import Namespace
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from torch.utils.data import DataLoader, TensorDataset
from transfor... | 223 | 1 |
"""simple docstring"""
UpperCAmelCase_ : Tuple = [
'''VerificationMode''',
'''Version''',
'''disable_progress_bar''',
'''enable_progress_bar''',
'''is_progress_bar_enabled''',
'''experimental''',
]
from .info_utils import VerificationMode
from .logging import disable_... | 717 |
"""simple docstring"""
import importlib.util
import os
import platform
from argparse import ArgumentParser
import huggingface_hub
from .. import __version__ as version
from ..utils import (
is_accelerate_available,
is_flax_available,
is_safetensors_available,
is_tf_available,
is_torch_a... | 165 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
UpperCAmelCase_ = {
'''configuration_maskformer''': ['''MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MaskFormerConfig'''],
'''configuration_maskformer_swi... | 271 | import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionSAGPipeline,
UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.... | 271 | 1 |
"""simple docstring"""
import numpy as np
def lowercase_ ( _lowerCamelCase: Tuple , _lowerCamelCase: Union[str, Any] , _lowerCamelCase: List[str] , _lowerCamelCase: Tuple , _lowerCamelCase: Union[str, Any] ) -> List[Any]:
'''simple docstring'''
__low... | 366 | """simple docstring"""
import os
def lowercase_ ( ) -> List[str]:
'''simple docstring'''
__lowerCamelCase : Union[str, Any] = os.path.dirname(os.path.realpath(_lowerCamelCase ) )
__lowerCamelCase : int = os.path.join(_lowerCamelCase , ... | 366 | 1 |
def __UpperCamelCase ( A ):
if len(A ) < 2:
return collection
def circle_sort_util(A , A , A ) -> bool:
UpperCamelCase__ = False
if low == high:
return swapped
UpperCamelCase__ = low
... | 415 | import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
... | 415 | 1 |
"""simple docstring"""
def lowerCamelCase__ ( _lowerCamelCase : Dict ) -> Dict:
lowerCamelCase_ , lowerCamelCase_ = [], []
while len(_lowerCamelCase ) > 1:
lowerCamelCase_ , lowerCamelCase_ = min(_lowerCamelCase ), max(_lowerCamelCase )... | 137 |
"""simple docstring"""
import math
def lowerCamelCase__ ( _lowerCamelCase : 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... | 137 | 1 |
from manim import *
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def UpperCamelCase_ ( self : str ):
__A = Rectangle(height=0.5 ,width=0.5 )
__A = Rectangle(height=0.46 ,width=0.46 ).set_stroke(width=... | 55 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
a__ : str = {
'configuration_roformer': ['ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP'... | 188 | 0 |
"""simple docstring"""
class lowerCAmelCase :
"""simple docstring"""
def __init__( self , UpperCamelCase__ , UpperCamelCase__ ) -> str:
'''simple docstring'''
lowerCamelCase_ = name
lowerCamelCase_ = va... | 66 |
"""simple docstring"""
import heapq as hq
import math
from collections.abc import Iterator
class lowerCAmelCase :
"""simple docstring"""
def __init__( self , UpperCamelCase__ ) -> str:
'''simple docstring'''
lowerCamelCase_ =... | 66 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_lowerCamelCase : Any = {"""configuration_xlnet""": ["""XLNET_PRETRAINED_CONFI... | 87 |
def SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ ) -> int:
"""simple docstring"""
return int((input_a, input_a).count(1 ) != 0 )
def SCREAMING_SNAKE_CASE ( ) -> None:
"""simple docstring"""
assert or_gate(0 , 0 ) == 0
... | 87 | 1 |
import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel
class lowerCAmelCase_ ( lowercase ):
"""simple docstring"""
_snake_case : int = """M-CLIP"""
def __init__( self :Any , lowerCamelCase__ :Dict=10_2... | 383 |
import json
import logging
import os
import sys
from time import time
from unittest.mock import patch
from transformers.testing_utils import TestCasePlus, require_torch_tpu
logging.basicConfig(level=logging.DEBUG)
UpperCamelCase = logging.getLogger()
def A ( lowercase__ : ... | 383 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE__ : Optional[int] = {
'configuration_swinv2': ['SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Swinv2Config'],
}
try:
if not is_torch_available()... | 311 |
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
SCREAMING_SNAKE_CASE__ : List[Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_... | 311 | 1 |
import argparse
import requests
import torch
from PIL import Image
from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor
def a_ ( _A ) -> List[str]:
"""simple docstring"""
snake_case__ = SwinConfig(image_size=192 )
... | 372 |
import doctest
import logging
import os
import unittest
from pathlib import Path
from typing import List, Union
import transformers
from transformers.testing_utils import require_tf, require_torch, slow
__UpperCamelCase : Optional[Any] = logging.getLogger()
@unittest.skip("T... | 372 | 1 |
'''simple docstring'''
import inspect
import unittest
class _a ( unittest.TestCase ):
'''simple docstring'''
def UpperCamelCase_ ( self ):
'''simple docstring'''
tr... | 28 |
'''simple docstring'''
def lowercase__( __UpperCamelCase: int ):
"""simple docstring"""
if not isinstance(__UpperCamelCase ,__UpperCamelCase ):
raise TypeError('Input value must be an \'int\' type' )
SCREAMING_SNAKE_CASE : i... | 28 | 1 |
"""simple docstring"""
def lowerCAmelCase_ ( UpperCamelCase__ : int = 1000 ):
"""simple docstring"""
__lowercase , __lowercase = 1, 1
__lowercase = []
for i in range(1 , n + 1 ):
__lowercase = prev_numerator + 2 * prev_denominator
... | 716 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
UpperCAmelCase__ ={"configuration_van": ["VAN_PRETRAINED_CONFIG_ARCHIVE_MAP", "VanConfig"]}
try:
if not is_torch_availa... | 442 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ : Union[str, Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : Optional[Any] = {
"""google/vivit-b-16x2-kinetics400""": (
"""https://huggingface.co/google/vivit-... | 0 | '''simple docstring'''
import sys
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific note... | 435 | 0 |
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from... | 664 |
import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type
from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterators import ThreadedIterator
from tqdm imp... | 664 | 1 |
"""simple docstring"""
from random import randint
from tempfile import TemporaryFile
import numpy as np
def __snake_case ( _lowercase ,_lowercase ,_lowercase ):
"""simple docstring"""
UpperCamelCase = 0
if start < end:
UpperCamelCase ... | 34 |
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()
# Step 1 - Convoluti... | 40 | 0 |
def __a ( __UpperCAmelCase , __UpperCAmelCase ):
if number < 0 or shift_amount < 0:
raise ValueError('''both inputs must be positive integers''' )
a__ = str(bin(__UpperCAmelCase ) )
binary_number += "0" * shift_amount
return binary_number
def __a ... | 148 |
from __future__ import annotations
import unittest
import numpy as np
from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFMode... | 148 | 1 |
"""simple docstring"""
from __future__ import annotations
class UpperCamelCase :
"""simple docstring"""
def __init__( self : Tuple , _lowerCamelCase : str ):
A__ = order
# a_{0} ... a_{k}
... | 571 |
from ..utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_scipy_available,
is_torch_available,
is_torchsde_available,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ..utils.dummy_pt_objects impo... | 100 | 0 |
'''simple docstring'''
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class _lowerCAmelCase ( __A... | 717 |
'''simple docstring'''
import math
def _SCREAMING_SNAKE_CASE( snake_case_ : int ) ->list[int]:
'''simple docstring'''
_lowercase : Optional[int] = []
_lowercase : Any = 2
_lowercase : Li... | 411 | 0 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.generation import DisjunctiveConstraint
@require_torch
class _lowerCamelCase( unittest.TestCase ):
def UpperCamelCase ( ... | 89 |
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,
SkipDataLoader,
sk... | 612 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
__magic_name__ = {
'configuration_convnext': ['CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ConvNext... | 27 |
'''simple docstring'''
import argparse
import json
from typing import List
from ltp import LTP
from transformers.models.bert.tokenization_bert import BertTokenizer
def lowerCamelCase ( lowerCamelCase : Optional[Any]):
# This defines a "chinese character" as anything in the CJK Unico... | 27 | 1 |
import logging
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import arg_to_scheduler
from transformers import TrainingArguments
_SCREAMING_SNAKE_CASE = logging.getLogger(__name__)
@dataclass
class a ( A__ ):
"""sim... | 401 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional, Tuple
import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput
@dataclass
class lowercase ( A__ ):
"""simple doc... | 189 | 0 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING, TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
from transformers.pipelines import AudioClassificationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
... | 491 |
"""simple docstring"""
from pathlib import Path
import numpy as np
from PIL import Image
def __snake_case ( SCREAMING_SNAKE_CASE: np.ndarray ):
"""simple docstring"""
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = rgb[:, :, 0], rgb[:... | 491 | 1 |
_snake_case = '''Input must be a string of 8 numbers plus letter'''
_snake_case = '''TRWAGMYFPDXBNJZSQVHLCKE'''
def _UpperCamelCase ( snake_case__ ) -> bool:
if not isinstance(UpperCAmelCase_, UpperCAmelCase_ ):
__UpperCAmelCase : ... | 382 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaImgaImgPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, ... | 322 | 0 |
"""simple docstring"""
import unittest
from transformers import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, is_vision_available, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
sl... | 213 |
"""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,
requ... | 213 | 1 |
"""simple docstring"""
import faiss # 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 requests # noqa: F401 # Here to have a nice missing dependency error message early on
impor... | 607 |
"""simple docstring"""
import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory
from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
convert,
ensure_valid_input,
ge... | 607 | 1 |
import inspect
import os
import unittest
from dataclasses import dataclass
import torch
from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cuda, require_multi_gpu
from acc... | 714 | def UpperCAmelCase ( lowercase , lowercase ):
"""simple docstring"""
__lowercase = ''''''
for i in table:
res += inp[i - 1]
return res
def UpperCAmelCase ( lowercase ):
"""simple docstring"""
return data[1... | 522 | 0 |
import argparse
import re
from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints
from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model
from tra... | 43 |
import unittest
from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch
class UpperCamelCase_ ( unittest.TestCase ):
'''simple docstring'''
UpperCAmelCase__ = JukeboxTokenizer
UpperCAmelCase__ = {
'''artist''': '''Z... | 87 | 0 |
'''simple docstring'''
import os
import re
import shutil
import sys
import tempfile
import unittest
import black
lowerCamelCase = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))
import check_copies # noqa: E402
#... | 701 |
'''simple docstring'''
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List
import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor
from transformers import... | 454 | 0 |
"""simple docstring"""
def lowerCAmelCase_ ( UpperCamelCase__ : list[int] , UpperCamelCase__ : list[int] , UpperCamelCase__ : int ):
"""simple docstring"""
return not any(
neighbour == 1 and colored_vertices[i] == color
for i, neigh... | 616 |
"""simple docstring"""
from typing import Optional
from urllib.parse import quote
import huggingface_hub as hfh
from packaging import version
def lowerCAmelCase_ ( UpperCamelCase__ : str , UpperCamelCase__ : str , UpperCamelCase__ : Optional[str] = None ):
... | 616 | 1 |
import argparse
import intel_extension_for_pytorch as ipex
import torch
from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline
_lowerCamelCase =argparse.ArgumentParser("Stable Diffusion script with intel optimization", add_help=False)
parser.add_argument("--dpm", action="store_true", he... | 252 |
_lowerCamelCase =[sum(int(c, 10) ** 2 for c in i.__str__()) for i in range(10_00_00)]
def snake_case__ ( lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =0
while number:
# Increased Speed Slightly by checking every 5 digits togethe... | 252 | 1 |
def __UpperCAmelCase ( __a : list[int] ) -> Optional[Any]:
"""simple docstring"""
_a : Union[str, Any] = len(lowercase_ )
for i in range(lowercase_ ):
for j in range(i + 1 ,lowercase_ ):
if numbers[j] < nu... | 14 |
'''simple docstring'''
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,
ft... | 588 | 0 |
'''simple docstring'''
from __future__ import annotations
def UpperCAmelCase_ ( __lowercase : Union[str, Any] , __lowercase : Optional[Any] ) -> Optional[int]:
'''simple docstring'''
if partitions <= 0:
raise ValueError("partitions must be a positive ... | 720 |
'''simple docstring'''
import datasets
from .nmt_bleu import compute_bleu # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py
__SCREAMING_SNAKE_CASE :Dict = '''\
@INPROCEEDINGS{Papineni02bleu:a,
author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing ... | 119 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
import diffusers
from diffusers import (
AutoencoderKL,
EulerDiscreteScheduler,
StableDiffusionLatentUpscalePipeline,
StableDiffusionPipeline,
UNetaDConditi... | 10 |
from dataclasses import dataclass
from typing import Optional, Tuple
import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput
@dataclass
class snake_case_ ( __A ):
'''simple docstring'''
... | 488 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase : Any = logging.get_logger(__name__)
lowerCAmelCase : List[Any] = {
'''RWKV/rwkv-4-169m-pile''': '''https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json''',
... | 717 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional
import torch
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .modeling_utils import ModelMixin
@data... | 39 | 0 |
import logging
import torch
from accelerate import Accelerator
from arguments import EvaluationArguments
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentPa... | 240 |
from __future__ import annotations
import os
import tempfile
import unittest
from transformers import ConvBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, ... | 147 | 0 |
'''simple docstring'''
from ...processing_utils import ProcessorMixin
class a ( __lowerCAmelCase ):
"""simple docstring"""
__lowerCAmelCase = ["""image_processor""", """feature_extractor"""]
__lowerCAmelCase = """TvltImageProcessor"""
__lowerCAmelCase ... | 714 | '''simple docstring'''
import unittest
from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch
class a ( unittest.TestCase ):
"""simple docstring"""
__lowerCAmelCase = JukeboxTokenizer
__lowerCAmelCase = {
"""artist""... | 466 | 0 |
"""simple docstring"""
def a__ ( snake_case__ , snake_case__ ) -> str:
if number < 0 or shift_amount < 0:
raise ValueError("""both inputs must be positive integers""" )
lowerCamelCase = str(bin(snake_case__ ) )
binary_number += "0" * shif... | 543 |
"""simple docstring"""
def a__ ( ) -> int:
return 1
def a__ ( snake_case__ ) -> int:
return 0 if x < 0 else two_pence(x - 2 ) + one_pence()
def a__ ( snake_case__ ) -> int:
return 0 if x < 0 else five_pence(x - 5 ... | 543 | 1 |
'''simple docstring'''
def __lowerCamelCase ( _lowerCAmelCase ) -> Union[str, Any]:
_UpperCAmelCase = False
while is_sorted is False: # Until all the indices are traversed keep looping
_UpperCAmelCase = True
for i in range(0 , len(_lowerCAmelCase ) - 1 , 2 ): ... | 714 |
import os
from typing import Optional
import fsspec
from fsspec.archive import AbstractArchiveFileSystem
from fsspec.utils import DEFAULT_BLOCK_SIZE
class __SCREAMING_SNAKE_CASE ( lowercase):
__SCREAMING_SNAKE_CASE : str = """"""
__SCREAMING_SNAKE_CASE : str ... | 129 | 0 |
"""simple docstring"""
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
if is_torch_available():
im... | 512 |
"""simple docstring"""
def _A (__a ) -> list[list]:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : int = current_set.copy()
for row_index, row in enumerate(__a ):
SCREAMING_SNAKE_CASE_ : Tuple = row[0]
for column_index, co... | 512 | 1 |
'''simple docstring'''
import math
from datetime import datetime, timedelta
def __magic_name__ ( __UpperCAmelCase ) -> datetime:
'''simple docstring'''
__SCREAMING_SNAKE_CASE = year % 19
__SCREAMING_SNAKE_CASE = year % 4
__S... | 13 |
'''simple docstring'''
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import numpy as np
import pandas as pd
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
BartForSequen... | 13 | 1 |
from datetime import datetime
import requests
def _A ( _lowercase ) -> bytes:
"""simple docstring"""
__UpperCamelCase = 'https://downloadgram.net/wp-json/wppress/video-downloader/video?url='
__UpperCamelCase = requests.get(base_url + url ).json()[0]['urls'][0]... | 1 |
"""simple docstring"""
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class A__ ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
@staticmethod
@abstractmethod
def _SCREAMING_SNAKE_CASE ( _SCREAMING_SNAKE_CASE: Argume... | 293 | 0 |
import time
import warnings
from abc import ABC
from copy import deepcopy
from typing import Optional
import torch
from ..utils import add_start_docstrings, logging
a = logging.get_logger(__name__)
a = R'\n Args:\n input_ids (`torch.LongTensor` of shape `(batch_si... | 715 |
import argparse
import json
import os
import fairseq
import torch
from torch import nn
from transformers import (
SpeechaTextaConfig,
SpeechaTextaForCausalLM,
SpeechaTextaTokenizer,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFeatureExtractor,
Wava... | 650 | 0 |
'''simple docstring'''
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_trans... | 683 |
'''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class UpperCAmelCase ( a_ ):
"""simple docstring"""
A__ : str = ['image_processor', 'tokenizer']
A__ : Dict = 'CLIPImageProcessor... | 683 | 1 |
from ..utils import DummyObject, requires_backends
class _UpperCAmelCase ( metaclass=A__ ):
UpperCamelCase__ = ['''torch''', '''transformers''', '''onnx''']
def __init__( self , *a__ , **a__):
requires_backends(self , ['''torch''', '''transformers'... | 708 |
import contextlib
import csv
import json
import os
import sqlitea
import tarfile
import textwrap
import zipfile
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
import datasets
import datasets.config
@pytest.fixture(scope='''session''' )
def lowerCAmelCase__ ( )-> str:
A__ = ... | 526 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__UpperCAmelCase ={
"""configuration_canine""": ["""CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CanineConfig"""],
"""tokenization... | 337 |
"""simple docstring"""
def __a ( A ) -> List[str]:
'''simple docstring'''
A__ = [0] * len(A )
A__ = []
A__ = []
A__ = 0
for values in graph.values():
for i in values:
indegree[i] += 1
for... | 337 | 1 |
"""simple docstring"""
# flake8: noqa
# Lint as: python3
from typing import Dict, List, Optional, Type
from .. import config
from ..utils import logging
from .formatting import (
ArrowFormatter,
CustomFormatter,
Formatter,
PandasFormatter,
PythonFormatter,
TensorFormatter,
format_table,
... | 275 | """simple docstring"""
import torch
from torch import nn
class a ( nn.Module ):
def __init__( self : List[Any] , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : str , __lowerCAmelCase : List[Any] , __lowerCAmelCase : int , __lowerCAmelCase : List[Any]=1 ... | 275 | 1 |
"""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
_lowerCAmelCase = logging.get_logger(__name__)
_l... | 259 |
"""simple docstring"""
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,
Bar... | 259 | 1 |
def lowerCamelCase_ ( _lowercase ) -> str:
__A : List[Any] = ""
for ch in key:
if ch == " " or ch not in key_no_dups and ch.isalpha():
key_no_dups += ch
return key_no_dups
def lowerCamelCase_ ( _lowercase ) -> dict[str, st... | 709 | import inspect
import unittest
from math import floor
from transformers import CvtConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_configuration_common impor... | 387 | 0 |
from ...configuration_utils import PretrainedConfig
snake_case__ = {
"""google/tapas-base-finetuned-sqa""": (
"""https://huggingface.co/google/tapas-base-finetuned-sqa/resolve/main/config.json"""
),
"""google/tapas-base-finetuned-wtq""": (
"""https://huggingface.co/google/... | 583 |
class UpperCAmelCase :
def __init__( self : Union[str, Any] , lowerCAmelCase : str = "" , lowerCAmelCase : bool = False ):
# Mapping from the first character of the prefix of the node
lowercase : dict[str, RadixNode] ... | 583 | 1 |
import inspect
import os
import unittest
from dataclasses import dataclass
import torch
from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cuda, require_multi_gpu
from... | 712 |
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 .dat... | 198 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
snake_case = logging.get_logger(__name__)
class lowerCAmelCase ( UpperCamelCase_ ... | 378 |
'''simple docstring'''
import unittest
from transformers import 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_modeling_common import ModelTester... | 378 | 1 |
"""simple docstring"""
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
... | 709 |
"""simple docstring"""
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment
class __UpperCamelCas... | 16 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase : List[str] = logging.get_logger(__name__)
class A__ ( A__ ):
"""simple docstring"""
_lowercase = 'timm_backbone'
def __init__( self : Any , lowerCamelCase__ ... | 37 |
'''simple docstring'''
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import Callable, Dict, List, Tuple
import timm
import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNe... | 596 | 0 |
import numpy as np
class a__ :
def __init__( self , lowercase__=None , lowercase__=None , lowercase__=None , lowercase__=None , lowercase__=None ) -> Dict:
self.set_matricies(red=lowercase__ , green=lowercase__ ... | 704 |
def UpperCAmelCase ( lowerCAmelCase__ ):
'''simple docstring'''
assert column_title.isupper()
__A = 0
__A = len(lowerCAmelCase__ ) - 1
__A = 0
while index >= 0:
__A = (ord(column_title[index] ) - 64) * pow(26 ... | 205 | 0 |
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,
AutoModelWithLMHead,
... | 53 |
"""simple docstring"""
def snake_case_ ( A_ : list, A_ : list, A_ : int, A_ : int, A_ : int ):
'''simple docstring'''
if index == number_of_items:
return 0
_lowerCamelCase : int = ... | 83 | 0 |
"""simple docstring"""
import itertools
import os
from collections import Counter, defaultdict
from concurrent.futures import ThreadPoolExecutor, as_completed
import numpy as np
import datasets
from .execute import check_correctness
lowerCAmelCase__ = '''\
@misc{chen2021evaluating,
title=... | 544 |
"""simple docstring"""
import importlib
import os
import sys
# This is required to make the module import works (when the python process is running from the root of the repo)
sys.path.append('''.''')
def a__ ( _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
UpperCamelCase = t... | 544 | 1 |
from argparse import ArgumentParser
from . import BaseTransformersCLICommand
def __lowerCamelCase (UpperCAmelCase__ : Optional[int] ):
return DownloadCommand(args.model , args.cache_dir , args.force , args.trust_remote_code )
class lowercase ( _a ):
... | 403 |
'''simple docstring'''
from __future__ import annotations
from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass
@dataclass
class A :
lowercase_ = 42
lowercase_ = 42
class A ... | 22 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE = {"""openai-gpt""": """https://huggingface.co/openai-gpt/resolve/main/config.json"""}
class ... | 718 |
"""simple docstring"""
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ConvNextConfig, SegformerImageProcessor, UperNetConfig, UperNetForSemanticSegmentation
def __UpperCamelCase ( SCREAMING_SNAKE_CASE ... | 614 | 0 |
'''simple docstring'''
import argparse
import OmegaConf
import torch
from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel
def _UpperCamelCase (_lowerCamelCase : str , _lowerCamelCase : Optional[int] , _lowerCamelCase : Optional[Any] )-> Union[str, A... | 24 |
'''simple docstring'''
import torch
from transformers import AutoModel
class UpperCamelCase_ ( torch.nn.Module ):
"""simple docstring"""
def __init__( self : Any , _lowerCamelCase : Optional[int]="sayef/fsner-bert-base-uncased" ) -> List[An... | 664 | 0 |
# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# 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 appl... | 146 |
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... | 146 | 1 |
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, ByTaTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...test_tokenization_common import To... | 313 |
from transformers import BertTokenizerFast
from .custom_tokenization import CustomTokenizer
class __lowercase ( lowerCamelCase__ ):
__UpperCAmelCase = CustomTokenizer
pass
| 313 | 1 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_config... | 267 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_speech_available, is_torch_available
__A : Tuple = {
'configuration_audio_spectrogram_transformer': [
'AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP'... | 267 | 1 |
class _snake_case :
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_):
'''simple docstring'''
lowercase__ : Optional[Any] = name
lowercase__ : Any = value
lowercase__ : Union[str, Any] = weig... | 12 |
lowerCamelCase__ : dict[tuple[int, int, int], int] = {}
def UpperCamelCase ( lowercase_ , lowercase_ , lowercase_ ) -> int:
'''simple docstring'''
if late == 3 or absent == 2:
return 0
# if we have no days left, and have not failed any other rules,... | 12 | 1 |
"""simple docstring"""
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from ...utils import deprecate
from ..controlnet.pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipelin... | 393 |
"""simple docstring"""
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import (
... | 393 | 1 |
'''simple docstring'''
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
AutoConfig,
AutoImageProcessor,
CLIPConfig,
CLIPImageProcessor,
)
from transformers.testing_utils im... | 325 |
'''simple docstring'''
import tempfile
import unittest
import numpy as np
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_flax
if is_fla... | 325 | 1 |
"""simple docstring"""
def _a ( _SCREAMING_SNAKE_CASE ) -> Any:
if not nums: # Makes sure that the list is not empty
raise ValueError("""List is empty""" )
snake_case_ = sum(_SCREAMING_SNAKE_CASE ) / len(_SCREAMING_SNAKE_CASE ) # Calculate t... | 716 |
"""simple docstring"""
__SCREAMING_SNAKE_CASE : str = 'Input must be a string of 8 numbers plus letter'
__SCREAMING_SNAKE_CASE : Dict = 'TRWAGMYFPDXBNJZSQVHLCKE'
def _a ( _SCREAMING_SNAKE_CASE ) -> bool:
if not isinstance(_SCREAMING_SNAKE_CASE , _SCR... | 2 | 0 |
import os
from pathlib import Path
def UpperCamelCase ( )-> Tuple:
"""simple docstring"""
from torch.utils.cpp_extension import load
A__ = Path(_A ).resolve().parent.parent.parent / "kernels" / "deformable_detr"
A__ = [
root / filename
for... | 491 |
import unittest
import numpy as np
import torch
from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad
class UpperCamelCase ( unittest.TestCase ):
def __A ( self ):
A__ = 10
def __A ( self ):
... | 491 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
SCREAMING_SNAKE_CASE : List[Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE : ... | 715 |
'''simple docstring'''
class snake_case :
"""simple docstring"""
def __init__( self ) -> Tuple:
SCREAMING_SNAKE_CASE_ = 0
SCREAMING_SNAKE_CASE_ = 0
SCREAMING_SNAKE_CASE_ = {}
... | 238 | 0 |
UpperCAmelCase_ = [sum(int(c, 1_0) ** 2 for c in i.__str__()) for i in range(1_0_0_0_0_0)]
def SCREAMING_SNAKE_CASE_ ( _snake_case :int ) -> int:
_A = 0
while number:
# Increased Speed Slightly by checking every 5 digits together.
sum_of_digits_squared +=... | 2 |
'''simple docstring'''
from __future__ import annotations
__UpperCamelCase = [
[-1, 0], # left
[0, -1], # down
[1, 0], # right
[0, 1], # up
]
def _a ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCas... | 26 | 0 |
"""simple docstring"""
import doctest
from collections import deque
import numpy as np
class snake_case :
'''simple docstring'''
def __init__( self : Tuple ):
'''simple docstring'''
__UpperCAmelCase ... | 720 |
"""simple docstring"""
import argparse
import torch
from transformers import LxmertConfig, LxmertForPreTraining, load_tf_weights_in_lxmert
from transformers.utils import logging
logging.set_verbosity_info()
def lowerCamelCase_ ( UpperCAmelCase_ , UpperCAmelCase_ ... | 374 | 0 |
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