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 |
|---|---|---|---|---|
'''simple docstring'''
from scipy.stats import pearsonr
import datasets
lowerCamelCase_ = '''
Pearson correlation coefficient and p-value for testing non-correlation.
The Pearson correlation coefficient measures the linear relationship between two datasets. The calculation of the p-value relies on t... | 418 |
def lowercase ( _lowerCAmelCase , _lowerCAmelCase ):
UpperCAmelCase__ = 0
while b > 0:
if b & 1:
res += a
a += a
b >>= 1
return res
def lowercase ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ):
UpperCAmelCase__ = 0
while... | 392 | 0 |
"""simple docstring"""
from __future__ import annotations
import math
def _A ( _a : int ):
"""simple docstring"""
if num <= 0:
A = f'{num}: Invalid input, please enter a positive integer.'
raise ValueError(_a ... | 255 |
"""simple docstring"""
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
UpperCAmelCase =get_logger(__name__)
UpperCAmelCase =R"\n Args:\n input_ids (`jnp.ndarray` of... | 255 | 1 |
'''simple docstring'''
import unittest
from transformers import MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, AutoTokenizer, is_vision_available
from transformers.pipelines import pipeline
from transformers.pipelines.document_question_answering import apply_tesseract
from transformers.testin... | 8 |
"""simple docstring"""
import warnings
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
class __snake_case ( _SCREAMING_SNAKE_CASE ):
... | 388 | 0 |
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class A_ ( a_ ):
_SCREAMING_SNAKE_CASE ... | 525 | 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()
clas... | 525 | 1 |
import unittest
from transformers import (
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
Pipeline,
ZeroShotClassificationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow
... | 149 |
import os
import unittest
from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class lowerCAmelCase ( __a , unittest.TestCase ):
'''simple docstring'''
_A : Un... | 149 | 1 |
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image
from transformers import (
Effic... | 664 |
import gc
import unittest
import numpy as np
import torch
from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determi... | 664 | 1 |
import argparse
import torch
from transformers import (
SpeechTaConfig,
SpeechTaFeatureExtractor,
SpeechTaForSpeechToSpeech,
SpeechTaForSpeechToText,
SpeechTaForTextToSpeech,
SpeechTaProcessor,
SpeechTaTokenizer,
logging,
)
from transformers.tokenization_utils import AddedToken
lo... | 691 |
def lowerCamelCase( a__ ,a__):
return int((input_a, input_a).count(0) == 0)
def lowerCamelCase( ):
assert and_gate(0 ,0) == 0
assert and_gate(0 ,1) == 0
assert and_gate(1 ,0) == 0
assert and_gate(1 ,1) == 1
if __name__ == "__main__":
test_and_gate(... | 691 | 1 |
"""simple docstring"""
import numpy as np
import torch
from torch.utils.data import Dataset
from utils import logger
class _A ( _UpperCAmelCase ):
"""simple docstring"""
def __init__( self : Any , A_ : Union[str, Any] , A_ :... | 93 | """simple docstring"""
import argparse
from pathlib import Path
import torch
from packaging import version
from torch.onnx import export
from diffusers import AutoencoderKL
__lowercase : int = version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")
... | 93 | 1 |
def UpperCAmelCase ( a_ , a_ , a_ ) -> Optional[Any]:
"""simple docstring"""
if n == 0:
return 1
elif n % 2 == 1:
return (binary_exponentiation(__a , n - 1 , __a ) * a) % mod
else:
__A = binary_exponentiation(_... | 55 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a__ = logging.get_logger(__name__)
a__ = {
'''funnel-transformer/small''': '''https://huggingface.co/funnel-transformer/small/resolve/main/config.json''',
'''funnel-transformer/small-b... | 14 | 0 |
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_tensorflow_text, require_t... | 720 |
import os
from datetime import datetime as dt
from github import Github
lowerCamelCase_ = [
'''good first issue''',
'''good second issue''',
'''good difficult issue''',
'''enhancement''',
'''new pipeline/model''',
'''new scheduler''',
'''wip''',
]
def __magic_name__ ( ... | 86 | 0 |
'''simple docstring'''
def lowercase__( __UpperCamelCase: int ,__UpperCamelCase: int ):
"""simple docstring"""
if number < 0 or shift_amount < 0:
raise ValueError('both inputs must be positive integers' )
SCREAMING_SNAKE_CASE : ... | 28 |
'''simple docstring'''
import inspect
import unittest
from transformers import MobileViTConfig
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_configura... | 28 | 1 |
'''simple docstring'''
from ....configuration_utils import PretrainedConfig
from ....utils import logging
_snake_case : Tuple = logging.get_logger(__name__)
_snake_case : str = {
'speechbrain/m-ctc-t-large': 'https://huggingface.co/speechbrain/m-ctc-t-large/res... | 717 |
'''simple docstring'''
import os
import pytest
from transformers.dynamic_module_utils import get_imports
_snake_case : List[str] = '\nimport os\n'
_snake_case : Dict = '\ndef foo():\n import os\n return False\n'
_snake_case : List[Any] =... | 377 | 0 |
from __future__ import annotations
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import is_tf_available, is_vision_available
from ...test_modeling_tf_common import floats_tensor, ids_tensor,... | 629 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
__lowerCamelCase : str = {"""tokenization_byt5""": ["""ByT5Tokenizer"""]}
if TYPE_CHECKING:
from .tokenization_byta import ByTaTokenizer
else:
import sys
__lowerCamelCase : List[str] = _LazyModule(__name__, glo... | 629 | 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 AddedToken, PreTrainedTokenizer
from ...utils import logging
lowerCamelCase__ = logging.get_logger(__name__)
lowerCamelCas... | 549 |
"""simple docstring"""
from __future__ import annotations
from collections import Counter
from random import random
class A__ :
def __init__( self ):
__lowerCAmelCase : Any = {}
def __lowerCamelCase ( self , _SCREAMING_SNAKE_CASE ):
__lowerCAmelCase : D... | 549 | 1 |
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 |
import unittest
from diffusers.pipelines.pipeline_utils import is_safetensors_compatible
class _UpperCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def lowercase ( self : Union[str, Any] ) -> List[str]:
__lowerCAmelCase = ... | 53 | 1 |
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class __A( SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = """Speech2TextFeatureExtractor"""
SCREAMING_SNAKE_CASE__ = """Speech2TextTokenizer"""
... | 719 |
from __future__ import annotations
from typing import TypedDict
class __A( __lowerCamelCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = 42
SCREAMING_SNAKE_CASE__ = 42
def __magic_name__ ( __a : str ):
'''simple docstring'''
if not... | 86 | 0 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFCamembertModel
@requi... | 699 |
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.uti... | 699 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
__A : Dict = {"""configuration_deit""": ["""DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DeiTConfig""", """DeiTOnnxCo... | 700 |
import gc
import importlib.metadata
import tempfile
import unittest
from packaging import version
from transformers import (
AutoModel,
AutoModelForCausalLM,
AutoModelForSeqaSeqLM,
AutoModelForSequenceClassification,
AutoTokenizer,
BitsAndBytesConfig,
pipeline,
)
from transformers.tes... | 450 | 0 |
"""simple docstring"""
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, PLBartTokenizer, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_... | 308 |
"""simple docstring"""
def lowercase__ ( lowerCamelCase : list , lowerCamelCase : int = 0 ) -> list:
lowerCAmelCase__ : List[Any] = length or len(lowerCamelCase )
lowerCAmelCase__ : Optional[Any] = False
for i in... | 308 | 1 |
"""simple docstring"""
import baseaa
import io
import json
import os
from copy import deepcopy
from ..optimizer import AcceleratedOptimizer
from ..scheduler import AcceleratedScheduler
class snake_case :
def __init__(self , SCREAMING_SNAKE_CASE_ ):
"""simple docstring"""... | 628 |
"""simple docstring"""
import torch
from diffusers import DDIMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class snake_case ( __lowercase ):
UpperCAmelCase__ = (DDIMParallelScheduler,)
UpperCAmelCase__ = (('''eta''', 0.0), ('''nu... | 628 | 1 |
"""simple docstring"""
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XGLMTokenizer, XGLMTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
from ..... | 363 |
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 OptionalDependencyNotAvailable()
e... | 513 | 0 |
'''simple docstring'''
from __future__ import annotations
def SCREAMING_SNAKE_CASE__ ( __A , __A ) -> str:
# Checks if the entire collection has been sorted
if len(lowerCAmelCase__ ) <= 1 or n <= 1:
return
insert_next(lowerCAmelCase__ , n - 1 )
rec_insertion_... | 721 |
'''simple docstring'''
import math
def SCREAMING_SNAKE_CASE__ ( __A , __A ) -> float:
if (
not isinstance(__A , (int, float) )
or power_factor < -1
or power_factor > 1
):
raise ValueError('power_factor must be a valid float value between -1 and 1.' ... | 542 | 0 |
"""simple docstring"""
# flake8: noqa
# Lint as: python3
a_ = [
'VerificationMode',
'Version',
'disable_progress_bar',
'enable_progress_bar',
'is_progress_bar_enabled',
'experimental',
]
from .info_utils import VerificationMode
from .logging import di... | 76 |
_lowercase : Dict = {
"Pillow": "Pillow",
"accelerate": "accelerate>=0.11.0",
"compel": "compel==0.1.8",
"black": "black~=23.1",
"datasets": "datasets",
"filelock": "filelock",
"flax": "flax>=0.4.1",
"hf-doc-builder": "hf-doc-builder>=0.3.0",
"huggingface-hub": "... | 641 | 0 |
"""simple docstring"""
import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey
def A__ ( _UpperCAmelCase : int , _UpperCAmelCase : int , _UpperCAmelCase : int , _UpperCAmelCase : int , ... | 150 |
"""simple docstring"""
from abc import ABC, abstractmethod
from typing import Optional, Union
from .. import Dataset, DatasetDict, Features, IterableDataset, IterableDatasetDict, NamedSplit
from ..utils.typing import NestedDataStructureLike, PathLike
class SCREAMING_SNAKE_CASE_ ( _lowercase):
... | 150 | 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 transformers.models.switch_t... | 42 | 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.test_... | 321 | 0 |
import argparse
import os
import re
import torch
from flax.traverse_util import flatten_dict
from tax import checkpoints
from transformers import (
AutoTokenizer,
PixaStructConfig,
PixaStructForConditionalGeneration,
PixaStructImageProcessor,
PixaStructProcessor,
PixaStructTextConfig,
Pi... | 707 |
import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey
def A ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
'''simple... | 658 | 0 |
import logging
import os
import threading
import time
try:
import warnings
except ImportError:
_lowerCAmelCase: Optional[int] = None
try:
import msvcrt
except ImportError:
_lowerCAmelCase: List[str] = None
try:
import fcntl
except ImportError:
_lowerCAmelCase: Dict ... | 20 |
import json
import os
import re
import unicodedata
from json.encoder import INFINITY
from typing import Any, Dict, List, Optional, Tuple, Union
import numpy as np
import regex
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding
from ...utils import T... | 611 | 0 |
"""simple docstring"""
from typing import List
import datasets
from datasets.tasks import AudioClassification
from ..folder_based_builder import folder_based_builder
snake_case__ : int = datasets.utils.logging.get_logger(__name__)
class snake_case_( folder_based_builder.FolderBased... | 704 |
"""simple docstring"""
class snake_case_:
def __init__( self : Union[str, Any] , UpperCamelCase_ : str ):
lowerCAmelCase : Dict = val
lowerCAmelCase : str = None
lowerCAmelCase : Dict = None
def ... | 637 | 0 |
'''simple docstring'''
import torch
def snake_case_ ( ):
'''simple docstring'''
if torch.cuda.is_available():
_snake_case = torch.cuda.device_count()
else:
_snake_case = 0
print(f'''Successfully ran on {num_gpus} GPUs''' )
if __name__ == "__main__":
... | 672 |
"""simple docstring"""
import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch
from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search
from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
from ut... | 355 | 0 |
import re
import string
from collections import Counter
import sacrebleu
import sacremoses
from packaging import version
import datasets
__a : Optional[int] = '\n@inproceedings{xu-etal-2016-optimizing,\n title = {Optimizing Statistical Machine Translation for Text Simplification}... | 709 |
import argparse
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline
if __name__ == "__main__":
__a : str = argparse.ArgumentParser()
parser.add_argument('''--dump_path''', default=N... | 298 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE_ = {'configuration_opt': ['OPT_PRETRAINED_... | 34 |
from __future__ import annotations
from typing import Any
class UpperCAmelCase__ :
'''simple docstring'''
def __init__( self : Dict , UpperCamelCase : int = 6 ):
"""simple docstring"""
_lowercase : Node | None ... | 322 | 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
__lowerCAmelCase = logging.get_logger(__name__)
__lowerCAmelCase = r"\n Args:\n input_ids (`torch.L... | 129 |
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
__lowerCAmelCase ... | 129 | 1 |
import warnings
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import Batch... | 27 |
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... | 27 | 1 |
import math
def UpperCAmelCase_ ( snake_case__ , snake_case__ ) -> float:
"""simple docstring"""
return math.pow(_lowerCAmelCase , 2 ) - a
def UpperCAmelCase_ ( snake_case__ ) -> float:
"""simple docstring"""
return 2 * x
... | 716 |
def UpperCAmelCase_ ( snake_case__ , snake_case__ ) -> float:
"""simple docstring"""
_validate_point(snake_case__ )
_validate_point(snake_case__ )
if len(snake_case__ ) != len(snake_case__ ):
raise ValueError('Both points must be in the same n-dimens... | 604 | 0 |
"""simple docstring"""
import unittest
import numpy as np
import torch
from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class a ( un... | 409 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModel,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import Autoencod... | 409 | 1 |
"""simple docstring"""
from __future__ import annotations
import numpy as np
def lowercase ( a__ : list[float] ) -> int:
return np.maximum(0 , a__ )
if __name__ == "__main__":
print(np.array(relu([-1, 0, 5]))) # --> [0, 0, 5]
| 342 | """simple docstring"""
from typing import Any
import numpy as np
def lowercase ( a__ : np.ndarray ) -> bool:
return np.array_equal(a__ , matrix.conjugate().T )
def lowercase ( a__ : np.ndarray , a__ : np.ndarray ) -> Any:
_UpperCamelCase = v.conjuga... | 342 | 1 |
"""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 ... | 58 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e im... | 259 | 0 |
from __future__ import annotations
def a__ ( __UpperCamelCase ): # This function is recursive
SCREAMING_SNAKE_CASE_ = len(__A )
# If the array contains only one element, we return it (it's the stop condition of
# recursion)
if array_length <= 1:
return array
... | 715 | from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
A : Dict = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
... | 356 | 0 |
"""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, C... | 510 |
"""simple docstring"""
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
_snake_case = ... | 510 | 1 |
'''simple docstring'''
from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import pyarrow as pa
if TYPE_CHECKING:
from .features import FeatureType
@dataclass
class lowerCamelCase :
'''simple docstring'''
l... | 719 |
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_dimension_format,
)
fr... | 23 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a = {
"configuration_swinv2": ["SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP", "Swinv2Config"],
}
try:
if not is_torch_available():
raise OptionalDepende... | 109 |
'''simple docstring'''
import logging
import os
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import List, Optional
import faiss
import torch
from datasets import Features, Sequence, Value, load_dataset
from t... | 109 | 1 |
import unittest
from transformers.testing_utils import CaptureStdout
from transformers.tools.python_interpreter import evaluate
def __a ( __UpperCAmelCase ):
return x + 2
class __UpperCamelCase ( unittest.TestCase ):
"""simple docstring"""
def _UpperCAmelCase ( sel... | 719 |
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_ : Any = {'configuration_xglm': ['XGLM_PRETRAINED_CONFIG... | 148 | 0 |
'''simple docstring'''
import unittest
from transformers import MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, AutoTokenizer, is_vision_available
from transformers.pipelines import pipeline
from transformers.pipelines.document_question_answering import apply_tesseract
from transformers.testing_utils import (
... | 42 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from... | 200 | 0 |
"""simple docstring"""
def _snake_case ( snake_case__ : str , snake_case__ : Any , snake_case__ : str=False ):
if isinstance(snake_case__ , snake_case__ ) and isinstance(snake_case__ , snake_case__ ):
A = len(set_a.intersection(snake_case__ ... | 22 |
"""simple docstring"""
from typing import Optional, Tuple, Union
import torch
from einops import rearrange, reduce
from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
from diffusers.sch... | 22 | 1 |
"""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,
... | 586 | """simple docstring"""
from __future__ import annotations
from typing import Any
def SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase ) -> int:
if not postfix_notation:
return 0
lowercase__: int = {'''+''', '''-''', '''*''', '''/'''}
lowercase__: list[Any] = []
for token in p... | 586 | 1 |
"""simple docstring"""
from ...utils import deprecate
from ..controlnet.pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline # noqa: F401
deprecate(
'stable diffusion controlnet',
'0.22.0',
'Importing `FlaxStableDiffusionControlNetPipeline` from diffusers.pipelines.stable_diffu... | 716 |
"""simple docstring"""
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import D... | 244 | 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,
... | 54 |
'''simple docstring'''
import os
import textwrap
import pyarrow as pa
import pytest
from datasets import ClassLabel, Features, Image
from datasets.packaged_modules.csv.csv import Csv
from ..utils import require_pil
@pytest.fixture
def _snake_case ( _SCREAMING_SNAKE_CASE... | 433 | 0 |
"""simple docstring"""
import logging
import numpy as np
import pytest
from scipy.linalg import eigh
logging.basicConfig(level=logging.INFO, format="""%(message)s""")
def _A ( __lowercase ):
"""simple docstring"""
return input_array.reshape((input_array.size, 1) )
... | 258 |
"""simple docstring"""
from math import sqrt
def _A ( __lowercase ):
"""simple docstring"""
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 e... | 258 | 1 |
'''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
#
... | 44 |
"""simple docstring"""
from ...utils import logging
from ..ta.modeling_tf_ta import TFTaEncoderModel, TFTaForConditionalGeneration, TFTaModel
from .configuration_mta import MTaConfig
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ = '''T5Config'''
class Upper... | 95 | 0 |
"""simple docstring"""
from ...processing_utils import ProcessorMixin
class SCREAMING_SNAKE_CASE_ ( _lowercase):
'''simple docstring'''
__magic_name__ : str = '''SpeechT5FeatureExtractor'''
__magic_name__ : List[Any] = '''Speec... | 720 |
"""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
... | 150 | 0 |
"""simple docstring"""
import unittest
from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...... | 308 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from transformers import XLMRobertaTokenizer
from diffusers import (
AltDiffusionImgaImgPipeline,
AutoencoderKL,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.image_pro... | 308 | 1 |
import json
import sys
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ , lowerCamelCase__ ) -> str:
with open(lowerCamelCase__ , encoding='utf-8' ) as f:
__lowerCamelCase : str = json.load(lowerCamelCase__ )
__lowerCamelCase : ... | 337 |
import tempfile
import torch
from diffusers import PNDMScheduler
from .test_schedulers import SchedulerCommonTest
class A_ ( SCREAMING_SNAKE_CASE ):
_UpperCAmelCase : int = (PNDMScheduler,)
_UpperCAmelCase : str = (('''num_inference_steps'''... | 337 | 1 |
'''simple docstring'''
def __a ( lowerCAmelCase__ : str ):
a__ : Tuple = 0
# if input_string is "aba" than new_input_string become "a|b|a"
a__ : str = ''''''
a__ : int = ''''''
# append each character + "|" in new_strin... | 688 |
'''simple docstring'''
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_accelera... | 688 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__SCREAMING_SNAKE_CASE : Optional[int] = {
'configuration_conditional_detr': [
'CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP'... | 2 |
"""simple docstring"""
import inspect
import unittest
from transformers import ConvNextVaConfig
from transformers.models.auto import get_values
from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES
from transformers.testing_utils import require_torch, require_visio... | 2 | 1 |
'''simple docstring'''
import pytest
import datasets
# Import fixture modules as plugins
_UpperCamelCase : Union[str, Any] =["tests.fixtures.files", "tests.fixtures.hub", "tests.fixtures.fsspec"]
def lowerCamelCase_ ( A_ , A_ ):
# Mark tests as "unit" by default if not ma... | 316 |
'''simple docstring'''
import importlib.metadata
from typing import Union
from packaging.version import Version, parse
from .constants import STR_OPERATION_TO_FUNC
_UpperCamelCase : Union[str, Any] =parse(importlib.metadata.version("torch"))
def lowerCamelCase_ ( A_ , A_ ... | 316 | 1 |
import unittest
from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow
if is_flax_available():
import optax
from flax.training.common_utils import onehot
from transformers import AutoTokenizer, FlaxMTaForCo... | 704 |
"""simple docstring"""
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartaaTokenizer, MBartaaTokenizerFast, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenize... | 282 | 0 |
"""simple docstring"""
from math import ceil
def A ( snake_case__ , snake_case__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = list(range(0 , _UpperCamelCase ) )
SCREAMING_SNAKE_CASE__ = [item for sublist in list(dev... | 196 |
"""simple docstring"""
import os
from datetime import datetime as dt
from github import Github
A__ : Tuple = [
'good first issue',
'good second issue',
'good difficult issue',
'enhancement',
'new pipeline/model',
'new scheduler',
'wip',
]
def _lowerCAmelCase ( ):
... | 353 | 0 |
from ...processing_utils import ProcessorMixin
class __lowercase( SCREAMING_SNAKE_CASE ):
"""simple docstring"""
UpperCamelCase_ = '''SpeechT5FeatureExtractor'''
UpperCamelCase_ = '''SpeechT5Tokenizer'''
def __init__( self : Opti... | 585 |
import requests
from bsa import BeautifulSoup
def _a ( __SCREAMING_SNAKE_CASE : str = "https://www.worldometers.info/coronavirus" ):
"""simple docstring"""
_lowerCAmelCase = BeautifulSoup(requests.get(__SCREAMING_SNAKE_CASE ).text , 'html.parser' )
_lowerC... | 585 | 1 |
"""simple docstring"""
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
... | 388 |
import json
import os
import re
import unittest
from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterM... | 137 | 0 |
# Lint as: python3
import itertools
import os
import re
__UpperCAmelCase = re.compile(r'([A-Z]+)([A-Z][a-z])')
__UpperCAmelCase = re.compile(r'([a-z\d])([A-Z])')
__UpperCAmelCase = re.compile(r'(?<!_)_(?!_)')
__UpperCAmelCase = re.compile(r'(_{2,})')
__UpperCAmelCase ... | 503 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaControlnetImgaImgPipeline,
KandinskyVaaPriorEmbaEmbPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, lo... | 503 | 1 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A = {'configuration_focalnet': ['FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FocalNetConfig']}
try:
if not is_torch_available():
raise OptionalDe... | 187 |
from __future__ import annotations
def a(lowercase__ , lowercase__ , lowercase__ , lowercase__ ):
'''simple docstring'''
if (direction == 1 and array[indexa] > array[indexa]) or (
direction == 0 and array[indexa] < array[indexa]
):
snake_case_ , snake_case_ = ... | 187 | 1 |
"""simple docstring"""
import argparse
import json
import numpy
import torch
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def lowercase ( __UpperCamelCase , __UpperCam... | 715 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vi... | 190 | 0 |
from ...processing_utils import ProcessorMixin
class __a ( __UpperCamelCase ):
__snake_case : Dict = ["""image_processor""", """feature_extractor"""]
__snake_case : Union[str, Any] = """TvltImageProcessor"""
__snake_case : int = """TvltFeatureExtractor"""
... | 600 |
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 __a ( __UpperCamelCase ):
__snake_cas... | 600 | 1 |
'''simple docstring'''
def a__ ( lowerCAmelCase__ ) -> str:
return "".join(chr(ord(lowerCAmelCase__ ) - 32 ) if '''a''' <= char <= '''z''' else char for char in word )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 720 |
'''simple docstring'''
def a__ ( lowerCAmelCase__ ) -> float:
if not nums: # Makes sure that the list is not empty
raise ValueError('''List is empty''' )
UpperCAmelCase__ : Tuple = sum(lowerCAmelCase__ ) / len(lowerCAmelCase__ ) # Calculate the average... | 312 | 0 |
"""simple docstring"""
_UpperCamelCase : Any = {str(digit): digit**5 for digit in range(10)}
def a_ ( _lowerCAmelCase : int ):
'''simple docstring'''
return sum(DIGITS_FIFTH_POWER[digit] for digit in str(_lowerCAmelCase ) )
def a_ ( ):
... | 599 | """simple docstring"""
import argparse
import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers import (
CLIPTokenizer,
CLIPTokenizerFast,
VideoMAEImageProcessor,
XCLIPConfig,
XCLIPModel,
XCLIPProcessor,
XCLIPTextConfig,
XCLIPVisio... | 599 | 1 |
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow
@require_torch
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
"""simple docstring"""
... | 451 |
from ....configuration_utils import PretrainedConfig
from ....utils import logging
lowercase__ : Tuple = logging.get_logger(__name__)
lowercase__ : Union[str, Any] = {
"speechbrain/m-ctc-t-large": "https://huggingface.co/speechbrain/m-ctc-t-large/resolve/main/config.j... | 451 | 1 |
from __future__ import annotations
def lowerCamelCase_ ( _lowercase , _lowercase = None , _lowercase = None , _lowercase = False , ) -> tuple[int, float, str]:
__A : int = cipher_alphabet or [chr(_lowercase ) for i in range(97 , 123 ... | 520 | 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,
)
UpperCamelCase = {'configuration_xglm': ['XGLM_PRE... | 520 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_snake_case : Any = logging.get_logger(__name__)
_snake_case : List[Any] = {
'google/switch-base-8': 'https://huggingface.co/google/switch-base-8/blob/main/config.json',
}
class ... | 700 |
_snake_case : List[Any] = [
999,
800,
799,
600,
599,
500,
400,
399,
377,
355,
333,
311,
288,
266,
244,
222,
200,
199,
177,
155,
133,
111,
88,
66,
44,
22,
0,
]
_snake_case : Lis... | 421 | 0 |
"""simple docstring"""
import inspect
import re
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
lowercase__ = """src/transfor... | 630 |
"""simple docstring"""
import argparse
import intel_extension_for_pytorch as ipex
import torch
from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline
lowercase__ = argparse.ArgumentParser("""Stable Diffusion script with intel optimization""", add_help=False)
pa... | 630 | 1 |
"""simple docstring"""
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel
from ...utils import logging
__A : List[str] = logging.get_logger(__name__)
def lowercase ( UpperCamelCase : Dict , UpperCamelCase : ... | 595 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__A : List[str] = {
"configuration_electra": ["ELECTRA_PR... | 595 | 1 |
'''simple docstring'''
from math import ceil
def __UpperCamelCase ( a : int = 1001 ) ->int:
snake_case = 1
for i in range(1 , int(ceil(n / 2.0 ) ) ):
snake_case = 2 * i + 1
snake_case = 2 * i
snake_case = total... | 342 |
'''simple docstring'''
def __UpperCamelCase ( a : Optional[int] , a : int , a : str=False ) ->List[str]:
if isinstance(a , a ) and isinstance(a , a ):
snake_case = len(set_a.intersection(a ) )
if alternative_unio... | 342 | 1 |
"""simple docstring"""
import inspect
import unittest
from huggingface_hub import hf_hub_download
from transformers import ASTConfig
from transformers.testing_utils import require_torch, require_torchaudio, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_torchaudio_avai... | 718 |
"""simple docstring"""
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_gpta import GPTaTok... | 524 | 0 |
from __future__ import annotations
def lowerCamelCase_ ( UpperCAmelCase__ ):
"""simple docstring"""
if len(UpperCAmelCase__ ) == 0:
return array
a_ , a_ = min(UpperCAmelCase__ ), max(UpperCAmelCase__ )
# Compute ... | 483 |
from dataclasses import asdict, dataclass
from typing import Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ : Any =logging.get_logger(__name__)
# TODO Update this
A_ : List[str] ={
"""facebook/esm-1b""": """http... | 483 | 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 logging
loggi... | 711 |
import json
import os
import re
import unicodedata
from json.encoder import INFINITY
from typing import Any, Dict, List, Optional, Tuple, Union
import numpy as np
import regex
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding
from ...utils import T... | 688 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase_ = logging.get_logger(__name__)
UpperCamelCase_ = {
"bigcode/gpt_bigcode-santacoder": "https://huggingface.co/bigcode/gpt_bigcode-santacoder/resolve/main/config.json",
}
class _SC... | 256 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase_ = logging.get_logger(__name__)
UpperCamelCase_ = {
"facebook/s2t-small-librispeech-asr": (
"https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/config.json"
... | 256 | 1 |
_snake_case = {
"""meter""": """m""",
"""kilometer""": """km""",
"""megametre""": """Mm""",
"""gigametre""": """Gm""",
"""terametre""": """Tm""",
"""petametre""": """Pm""",
"""exametre""": """Em""",
"""zettametre""": """Zm""",
"""yottametre""": """Ym""",
}
# Exponent o... | 611 |
import warnings
from ...utils import logging
from .image_processing_dpt import DPTImageProcessor
_snake_case = logging.get_logger(__name__)
class lowerCAmelCase ( lowercase_ ):
def __init__( self :Optional[int] , *_lowercase :Tuple , ... | 611 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A__ : Optional[Any] = {'configuration_vit_msn': ['VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTMSNConfig']}
try:
if not is_torch_available():
raise Opti... | 183 |
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():
raise OptionalDependencyNotAvailable... | 84 | 0 |
import multiprocessing
import os
from typing import BinaryIO, Optional, Union
import fsspec
from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, Pat... | 290 |
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onn... | 290 | 1 |
'''simple docstring'''
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE = {
"RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json",
}
class __Up... | 366 |
'''simple docstring'''
import math
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput
class __UpperCAmelCase ( lowerCAmelCase ,lowerCAmelCase ... | 366 | 1 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoFormerTokenizer
from .tokeni... | 622 |
import argparse
import csv
import logging
import os
import random
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange
from transformers import (
CONFIG_NAME,
WEIGHTS_NAME,
AdamW,
OpenAIGPTDoubleHeads... | 622 | 1 |
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
UpperCAmelCase_ : Dict = logging.get_logger... | 21 |
from __future__ import annotations
import unittest
from transformers import BlenderbotSmallConfig, BlenderbotSmallTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common i... | 311 | 0 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import requ... | 491 |
"""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
_snake_case = logging.get_logger(__name__)
_snake_ca... | 491 | 1 |
'''simple docstring'''
import unittest
from transformers import AutoTokenizer, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
if is_flax_available():
import jax.numpy as jnp
from transformers import FlaxXLMRobertaModel
@require_sentencepi... | 365 | '''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... | 107 | 0 |
import re
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class __snake_case ( lowerCamelCase_ ):
lowerCAmelCase_ = ["image_processor", "tokenizer"]
lowerCAmelCase_ = "AutoImageProcessor"
lo... | 379 | from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar
__lowerCamelCase : List[str] = TypeVar('''KEY''')
__lowerCamelCase : int = TypeVar('''VAL''')
@dataclass(frozen=lowerCamelCase_ , slots=lo... | 379 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCAmelCase = logging.get_logger(__name__)
_lowerCAmelCase = {
"microsoft/swinv2-tiny-patch4-window8-256": (
"https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256/resolve/main/config.json"
),... | 10 | from __future__ import annotations
_lowerCAmelCase = [True] * 1_000_001
_lowerCAmelCase = 2
while i * i <= 1_000_000:
if seive[i]:
for j in range(i * i, 1_000_001, i):
_lowerCAmelCase = False
i += 1
def _snake_case ( __snake_case ):
return seive[n... | 10 | 1 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__magic_name__ : List[str] = logging.get_logger(__name__)
__magic_name__ : ... | 712 |
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
__magic_name__ : Optional[Any] = logging.get_logger(__name__)
__magic_name__ : Tuple = {
'google/umt5-small': 'h... | 608 | 0 |
import time
from dataclasses import dataclass
from multiprocessing import Pool
from unittest import TestCase
from unittest.mock import patch
import multiprocess
import numpy as np
import pytest
from datasets.utils.py_utils import (
NestedDataStructure,
asdict,
iflatmap_unordered,
... | 623 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_snake_case : List[str] = logging.get_logger(__name__)
_snake_case : int = {
"google/switch-base-8": "https://huggingface.co/google/switch-base-8/blob/main/config.json",
}
c... | 441 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowercase : Optional[Any] =logging.get_logger(__name__)
__lowercase : Optional[int] ={
"""EleutherAI/gpt-neox-20b""": """https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/co... | 550 |
__lowercase : Dict ="""0.21.0"""
from .accelerator import Accelerator
from .big_modeling import (
cpu_offload,
cpu_offload_with_hook,
disk_offload,
dispatch_model,
init_empty_weights,
init_on_device,
load_checkpoint_and_dispatch,
)
from .data_loader imp... | 550 | 1 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSampling... | 348 |
# 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... | 348 | 1 |
"""simple docstring"""
from __future__ import annotations
import math
import random
from collections.abc import Collection
from typing import overload
class __magic_name__ :
def __init__( self : Tuple , snake_case__ : Collection[float] | None = None ):
'''simpl... | 475 |
"""simple docstring"""
import unittest
import numpy as np
def lowerCamelCase (a_ :np.ndarray , a_ :np.ndarray , a_ :np.ndarray , a_ :np.ndarray | None = None , ) -> np.ndarray:
lowercase :str = np.shape(a_)
lower... | 475 | 1 |
'''simple docstring'''
import argparse
import tensorflow as tf
import torch
from transformers import BertConfig, BertForMaskedLM
from transformers.models.bert.modeling_bert import (
BertIntermediate,
BertLayer,
BertOutput,
BertPooler,
BertSelfAttention,
BertSelfOutput,
)
from transformers.u... | 679 |
'''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()
a : Optional[Any] = logging.get_logger(__name__)
a : List[str] = {name: getattr(transformers, n... | 679 | 1 |
import gc
import unittest
import numpy as np
import torch
from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_d... | 718 |
from __future__ import annotations
import copy
import tempfile
import unittest
from transformers import CONFIG_MAPPING, AutoConfig, BertConfig, GPTaConfig, TaConfig, TapasConfig, is_tf_available
from transformers.testing_utils import (
DUMMY_UNKNOWN_IDENTIFIER,
SMALL_MODEL_IDENTIFIER,
RequestC... | 166 | 0 |
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
class __UpperCamelCase ( unittest.TestCase , A__ ):
def UpperCamelCase( self ):
_UpperCAmelCase = load_tool('''text-classification''' )
self.to... | 32 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoFormer... | 422 | 0 |
import unittest
from diffusers.models.unet_ad_blocks import * # noqa F403
from diffusers.utils import torch_device
from .test_unet_blocks_common import UNetBlockTesterMixin
class __SCREAMING_SNAKE_CASE ( _a , unittest.TestCase ):
snake_case : Optional[int] = ... | 548 |
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_gpu, require_vision, slow, t... | 548 | 1 |
'''simple docstring'''
from typing import Union
import fire
import torch
from tqdm import tqdm
def _SCREAMING_SNAKE_CASE ( __snake_case : str , __snake_case : str = "cpu" , __snake_case : Union[str, None] = None ):
_A = torch.load(__snake_case , map_loca... | 107 | '''simple docstring'''
from __future__ import annotations
import unittest
from transformers import 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, random_attentio... | 107 | 1 |
import gc
import random
import unittest
import torch
from diffusers import (
IFImgaImgPipeline,
IFImgaImgSuperResolutionPipeline,
IFInpaintingPipeline,
IFInpaintingSuperResolutionPipeline,
IFPipeline,
IFSuperResolutionPipeline,
)
from diffusers.models.attention_proces... | 708 |
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.tra... | 388 | 0 |
"""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_case_ ( low... | 34 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Callable
from typing import Any, Generic, TypeVar
__SCREAMING_SNAKE_CASE = TypeVar("""T""")
class __snake_case ( Generic[T] ):
"""simple docstring"""
def __init__( self :Tuple ... | 388 | 0 |
from torch import nn
def _UpperCAmelCase ( A ):
'''simple docstring'''
if act_fn in ["swish", "silu"]:
return nn.SiLU()
elif act_fn == "mish":
return nn.Mish()
elif act_fn == "gelu":
return nn.GELU()
else:
raise ValueError... | 510 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase_ = logging.get_logger(__name__)
UpperCamelCase_ = {
'bigcode/gpt_bigcode-santacoder': 'https://huggingface.co/bigcode/gpt_bigcode-santacoder/resolve/main/config.json',
}
cl... | 510 | 1 |
"""simple docstring"""
def lowerCamelCase_( _lowerCamelCase , _lowerCamelCase ) -> int:
'''simple docstring'''
return abs(_lowerCamelCase ) if a == 0 else greatest_common_divisor(b % a , _lowerCamelCase )
def lowerCamelCase_( _lowerCamelCase , ... | 46 |
"""simple docstring"""
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... | 46 | 1 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Callable
from typing import Any, Generic, TypeVar
UpperCAmelCase : List[str] = TypeVar('T')
class lowerCamelCase (Generic[T] ):
def __init__( self , lowercase__ , lowercase_... | 47 |
'''simple docstring'''
import math
from numpy import inf
from scipy.integrate import quad
def _a ( lowerCAmelCase_ ):
"""simple docstring"""
if num <= 0:
raise ValueError('''math domain error''' )
return quad(lowerCAmelCase_ , 0 , lowerCAmelCa... | 47 | 1 |
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