code stringlengths 82 54.1k | code_codestyle int64 0 699 | style_context stringlengths 111 35.6k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
class lowercase_ :
def __init__( self , lowercase_ , lowercase_=None , lowercase_=None) -> Tuple:
a__ =data
a__ =previous
a__ =next_node
def __str__( self) -> str:
return F"""{self.data}"""
de... | 20 |
import os
_lowercase = {'''I''': 1, '''V''': 5, '''X''': 10, '''L''': 50, '''C''': 100, '''D''': 500, '''M''': 1000}
def UpperCamelCase ( snake_case__):
lowerCAmelCase_ : List[str] = 0
lowerCAmelCase_ : Any = 0
while index < len(snake_case__) - 1:
... | 659 | 0 |
def lowerCAmelCase_ ( lowerCamelCase = 1000 ):
__magic_name__ : Optional[int] =-1
__magic_name__ : Tuple =0
for a in range(1 , n // 3 ):
# Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c
__magic_name__ ... | 21 |
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def UpperCamelCase ( ):
lowerCAmelCase_ : Dict = HfArgumentParser(snake_case__)
lowerCAmelCase_ : Dict = parser.parse_args_into_dataclasses()[0]
lowerCAmelCase_ : Lis... | 659 | 0 |
'''simple docstring'''
import copy
import re
class A :
lowercase_ = 'hp'
lowercase_ = {}
lowercase_ = None
@classmethod
def __lowerCAmelCase ( cls : List[Any] , low... | 22 |
from collections import defaultdict
from pathlib import Path
import pandas as pd
from rouge_cli import calculate_rouge_path
from utils import calculate_rouge
_lowercase = [
'''Prosecutor: "No videos were used in the crash investigation" German papers say they saw a cell phone video of the'''
''' ... | 659 | 0 |
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
from .timesteps import (
fastaa_timesteps,
smartaa_timesteps,
smar... | 23 |
import json
import os
import unittest
from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property
from ...test_to... | 659 | 0 |
'''simple docstring'''
import argparse
import importlib
from pathlib import Path
# Test all the extensions added in the setup
UpperCAmelCase_ : Any = [
'''kernels/rwkv/wkv_cuda.cu''',
'''kernels/rwkv/wkv_op.cpp''',
'''kernels/deformable_detr/ms_deform_attn.h''',
'''kernels/deformab... | 24 |
from ....configuration_utils import PretrainedConfig
from ....utils import logging
_lowercase = logging.get_logger(__name__)
_lowercase = {
'''Visual-Attention-Network/van-base''': (
'''https://huggingface.co/Visual-Attention-Network/van-base/blob/main/config.json'''
),
}
class __... | 659 | 0 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_big_bird import ... | 25 |
from math import factorial
def UpperCamelCase ( snake_case__ , snake_case__):
# If either of the conditions are true, the function is being asked
# to calculate a factorial of a negative number, which is not possible
if n < k or k < 0:
raise ValueError("Please enter pos... | 659 | 0 |
'''simple docstring'''
import logging
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import librosa
import torch
from datasets import DatasetDict, load_dataset
from packaging import version
from torch import nn
from transform... | 26 |
import argparse
import json
from tqdm import tqdm
def UpperCamelCase ( ):
lowerCAmelCase_ : Any = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--src_path" , type=snake_case__ , default="biencoder-nq-dev.json" , help="Path... | 659 | 0 |
import math
import time
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
import torch_xla.debug.metrics as met
clas... | 27 |
from collections.abc import Sequence
def UpperCamelCase ( snake_case__ = None):
if nums is None or not nums:
raise ValueError("Input sequence should not be empty")
lowerCAmelCase_ : Dict = nums[0]
for i in range(1 , len(snake_case__)):
lowerCAme... | 659 | 0 |
'''simple docstring'''
UpperCamelCase_ = [
"Audio",
"Array2D",
"Array3D",
"Array4D",
"Array5D",
"ClassLabel",
"Features",
"Sequence",
"Value",
"Image",
"Translation",
"TranslationVariableLanguages",
]
from .audio import Audio
from .f... | 28 |
from typing import TYPE_CHECKING
from ....utils import _LazyModule
_lowercase = {'''tokenization_tapex''': ['''TapexTokenizer''']}
if TYPE_CHECKING:
from .tokenization_tapex import TapexTokenizer
else:
import sys
_lowercase = _LazyModule(__name__, globals()['''__file__'''... | 659 | 0 |
"""simple docstring"""
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pytest
from datasets.arrow_dataset import Dataset
from datasets.search import ElasticSearchIndex, FaissIndex, MissingIndex
from .utils import requi... | 29 |
import argparse
import glob
import importlib.util
import os
import re
import black
from doc_builder.style_doc import style_docstrings_in_code
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
_lowercase = '''src/diffuse... | 659 | 0 |
from collections import OrderedDict
from typing import Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...tokenization_uti... | 30 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase = logging.get_logger(__name__)
_lowercase = {
'''microsoft/swinv2-tiny-patch4-window8-256''': (
'''https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256/resolve/main/config.json'''
... | 659 | 0 |
import math
import qiskit
def UpperCAmelCase_ ( __UpperCAmelCase : int = 1 , __UpperCAmelCase : int = 1 , __UpperCAmelCase : int = 1 ) -> qiskit.result.counts.Counts:
if (
isinstance(__UpperCAmelCase , __UpperCAmelCase )
or isi... | 31 |
from typing import 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 BatchFeature
from ....file_utils import Paddi... | 659 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = {
"microsoft/focalnet-tiny": "https://hug... | 32 |
from __future__ import annotations
# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
_lowercase = 10
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__):
... | 659 | 0 |
from math import isqrt
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase ) -> bool:
return all(number % divisor != 0 for divisor in range(2 , isqrt(__lowerCAmelCase ) + 1 ) )
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase = 10**6 ) -> int:
snake_ca... | 33 |
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
_lowercase = logging.get_logger(__name__)
_lowercase = {
... | 659 | 0 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
... | 34 |
from collections.abc import Generator
from math import sin
def UpperCamelCase ( snake_case__):
if len(snake_case__) != 32:
raise ValueError("Input must be of length 32")
lowerCAmelCase_ : Tuple = b""
for i in [3, 2, 1, 0]:
little_endian += string_aa[8... | 659 | 0 |
import copy
import tempfile
import unittest
from transformers import MaMaaaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from transformers.utils import cached_property
from ...generation.test_utils import GenerationTes... | 35 |
import logging
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import librosa
import torch
from datasets import DatasetDict, load_dataset
from packaging import version
from torch import nn
from transformers import (
HfArgumentParser,
Trainer,
Trai... | 659 | 0 |
import argparse
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt
if __name__ == "__main__":
__lowercase : Dict = argparse.ArgumentParser()
parser.add_argument(
'''--checkpoint_path''', default=None, type=str, required=True,... | 36 |
from __future__ import annotations
from collections.abc import Callable
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__ = 1_00 , ):
lowerCAmelCase_ : Any = x_start
lowerCAmelCase_ : Optional[Any] = fnc(snake_case_... | 659 | 0 |
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.te... | 37 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
PNDMScheduler,
StableDiffusionLDMaDPipeline,
UNetaDConditionModel,
)
from diffusers.utils import nightly, slow, t... | 659 | 0 |
'''simple docstring'''
from math import ceil
from typing import List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import BatchFeature, SequenceFeatureExtractor
from ...utils import TensorType, logging... | 38 |
import argparse
import re
import numpy as np
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SamConfig,
SamImageProcessor,
SamModel,
SamProcessor,
SamVisionConfig,
)
_lowercase = {
'''iou_prediction_head.lay... | 659 | 0 |
# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless requ... | 39 |
class __snake_case :
"""simple docstring"""
def __init__( self : Union[str, Any] ,lowerCAmelCase__ : str = "" ,lowerCAmelCase__ : bool = False ) -> None:
'''simple docstring'''
lowerCAmelCase_ : dict[str, RadixNode] = {... | 659 | 0 |
from typing import TYPE_CHECKING
from ....utils import _LazyModule
__UpperCAmelCase = {'''tokenization_tapex''': ['''TapexTokenizer''']}
if TYPE_CHECKING:
from .tokenization_tapex import TapexTokenizer
else:
import sys
__UpperCAmelCase = _LazyModule(__name__, globals()['''__file__'''... | 40 |
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 TFModel... | 659 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
'''google/bit-... | 41 |
import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging
_lowerc... | 659 | 0 |
'''simple docstring'''
#
# This a `torch.distributed` diagnostics script that checks that all GPUs in the cluster (one or
# many nodes) can talk to each other via nccl and allocate gpu memory.
#
# To run first adjust the number of processes and nodes:
#
# python -m torch.distributed.run --nproc_per_node 2 --nnod... | 42 |
import os
_lowercase = {'''I''': 1, '''V''': 5, '''X''': 10, '''L''': 50, '''C''': 100, '''D''': 500, '''M''': 1000}
def UpperCamelCase ( snake_case__):
lowerCAmelCase_ : List[str] = 0
lowerCAmelCase_ : Any = 0
while index < len(snake_case__) - 1:
... | 659 | 0 |
import gc
import unittest
import numpy as np
import torch
from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
enable_full_determinism()
@slow
@require_torch_... | 43 |
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def UpperCamelCase ( ):
lowerCAmelCase_ : Dict = HfArgumentParser(snake_case__)
lowerCAmelCase_ : Dict = parser.parse_args_into_dataclasses()[0]
lowerCAmelCase_ : Lis... | 659 | 0 |
'''simple docstring'''
import argparse
import torch
from torch import nn
from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def A_ ( _lowerCAmelCase : List[str] ):
"""simple docstring"""
_lowerCamelCase : List[Any] = [
"encoder... | 44 |
from collections import defaultdict
from pathlib import Path
import pandas as pd
from rouge_cli import calculate_rouge_path
from utils import calculate_rouge
_lowercase = [
'''Prosecutor: "No videos were used in the crash investigation" German papers say they saw a cell phone video of the'''
''' ... | 659 | 0 |
import io
import json
import fsspec
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.json import JsonDatasetReader, JsonDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def A ( lowercase__ : ... | 45 |
import json
import os
import unittest
from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property
from ...test_to... | 659 | 0 |
"""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_configuration_common imp... | 46 |
from ....configuration_utils import PretrainedConfig
from ....utils import logging
_lowercase = logging.get_logger(__name__)
_lowercase = {
'''Visual-Attention-Network/van-base''': (
'''https://huggingface.co/Visual-Attention-Network/van-base/blob/main/config.json'''
),
}
class __... | 659 | 0 |
from collections.abc import Sequence
from queue import Queue
class _UpperCamelCase:
def __init__( self : Tuple , SCREAMING_SNAKE_CASE__ : List[str] , SCREAMING_SNAKE_CASE__ : List[Any] , SCREAMING_SNAKE_CASE__ : Tuple , SCREAMIN... | 47 |
from math import factorial
def UpperCamelCase ( snake_case__ , snake_case__):
# If either of the conditions are true, the function is being asked
# to calculate a factorial of a negative number, which is not possible
if n < k or k < 0:
raise ValueError("Please enter pos... | 659 | 0 |
'''simple docstring'''
# This is the module that test_patching.py uses to test patch_submodule()
import os # noqa: this is just for tests
import os as renamed_os # noqa: this is just for tests
from os import path # noqa: this is just for tests
from os import path as renamed_path # noqa: this is just for tests
fr... | 48 |
import argparse
import json
from tqdm import tqdm
def UpperCamelCase ( ):
lowerCAmelCase_ : Any = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--src_path" , type=snake_case__ , default="biencoder-nq-dev.json" , help="Path... | 659 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_lowercase : Dict = logging.get_logger(__name__)
_lowercase : List[Any] ... | 49 |
from collections.abc import Sequence
def UpperCamelCase ( snake_case__ = None):
if nums is None or not nums:
raise ValueError("Input sequence should not be empty")
lowerCAmelCase_ : Dict = nums[0]
for i in range(1 , len(snake_case__)):
lowerCAme... | 659 | 0 |
'''simple docstring'''
def A__ ( __lowerCAmelCase : list , __lowerCAmelCase : list , __lowerCAmelCase : int , __lowerCAmelCase : int , __lowerCAmelCase : int ):
if index == number_of_items:
return 0
lowerCamelCase__ ... | 50 |
from typing import TYPE_CHECKING
from ....utils import _LazyModule
_lowercase = {'''tokenization_tapex''': ['''TapexTokenizer''']}
if TYPE_CHECKING:
from .tokenization_tapex import TapexTokenizer
else:
import sys
_lowercase = _LazyModule(__name__, globals()['''__file__'''... | 659 | 0 |
'''simple docstring'''
import inspect
import unittest
from transformers import RegNetConfig
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 im... | 51 |
import argparse
import glob
import importlib.util
import os
import re
import black
from doc_builder.style_doc import style_docstrings_in_code
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
_lowercase = '''src/diffuse... | 659 | 0 |
"""simple docstring"""
import os
def __A ( ) -> Tuple:
with open(os.path.dirname(a_) + '''/grid.txt''') as f:
__a : Dict = [] # noqa: E741
for _ in range(20):
l.append([int(a_) for x in f.readline().split()])
__a ... | 52 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase = logging.get_logger(__name__)
_lowercase = {
'''microsoft/swinv2-tiny-patch4-window8-256''': (
'''https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256/resolve/main/config.json'''
... | 659 | 0 |
from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available
if is_datasets_available():
import datasets
class _UpperCAmelCase ... | 53 |
from typing import 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 BatchFeature
from ....file_utils import Paddi... | 659 | 0 |
import inspect
import re
from hashlib import shaaaa
from typing import Dict, List
from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql impo... | 54 |
from __future__ import annotations
# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
_lowercase = 10
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__):
... | 659 | 0 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Image
from .base import TaskTemplate
@dataclass(frozen=__SCREAMING_SNAKE_CASE )
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
snake... | 55 |
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
_lowercase = logging.get_logger(__name__)
_lowercase = {
... | 659 | 0 |
'''simple docstring'''
def _a (lowercase__ : str , lowercase__ : str ) -> Dict:
"""simple docstring"""
assert x is not None
assert y is not None
__snake_case = len(lowercase__ )
__snake_case = len(lowercase__ )
... | 56 |
from collections.abc import Generator
from math import sin
def UpperCamelCase ( snake_case__):
if len(snake_case__) != 32:
raise ValueError("Input must be of length 32")
lowerCAmelCase_ : Tuple = b""
for i in [3, 2, 1, 0]:
little_endian += string_aa[8... | 659 | 0 |
import unittest
from transformers import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING, is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
sl... | 57 |
import logging
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import librosa
import torch
from datasets import DatasetDict, load_dataset
from packaging import version
from torch import nn
from transformers import (
HfArgumentParser,
Trainer,
Trai... | 659 | 0 |
"""simple docstring"""
def __lowerCAmelCase ( __UpperCamelCase : int , __UpperCamelCase : int ):
'''simple docstring'''
if not isinstance(__UpperCamelCase , __UpperCamelCase ):
raise ValueError("""iterations mu... | 58 |
from __future__ import annotations
from collections.abc import Callable
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__ = 1_00 , ):
lowerCAmelCase_ : Any = x_start
lowerCAmelCase_ : Optional[Any] = fnc(snake_case_... | 659 | 0 |
import numpy as np
from transformers import Pipeline
def lowerCAmelCase_ ( __a ) -> List[str]:
"""simple docstring"""
lowerCamelCase__: Optional[Any] =np.max(__a , axis=-1 , keepdims=__a )
lowerCamelCase__: int =np.exp(outputs - m... | 59 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
PNDMScheduler,
StableDiffusionLDMaDPipeline,
UNetaDConditionModel,
)
from diffusers.utils import nightly, slow, t... | 659 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase_ = {
'''configuration_upernet''': ['''UperNetConfig'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependency... | 60 |
import argparse
import re
import numpy as np
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SamConfig,
SamImageProcessor,
SamModel,
SamProcessor,
SamVisionConfig,
)
_lowercase = {
'''iou_prediction_head.lay... | 659 | 0 |
from __future__ import annotations
def _A ( lowerCAmelCase_ : list , lowerCAmelCase_ : int , lowerCAmelCase_ : int , lowerCAmelCase_ : int ):
"""simple docstring"""
lowerCAmelCase__ = []
lowerCAmelCase__ , lowerCAmelCas... | 61 |
class __snake_case :
"""simple docstring"""
def __init__( self : Union[str, Any] ,lowerCAmelCase__ : str = "" ,lowerCAmelCase__ : bool = False ) -> None:
'''simple docstring'''
lowerCAmelCase_ : dict[str, RadixNode] = {... | 659 | 0 |
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 .dataclasses import... | 62 |
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 TFModel... | 659 | 0 |
def lowerCamelCase__ ( __lowerCamelCase : int ):
if not isinstance(__lowerCamelCase , __lowerCamelCase ):
__UpperCAmelCase : Tuple = f"""Input value of [number={number}] must be an integer"""
raise TypeError(__lowerCamelCase )
if numb... | 63 |
import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging
_lowerc... | 659 | 0 |
import inspect
import unittest
import warnings
from math import ceil, floor
from transformers import LevitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vis... | 64 |
import os
_lowercase = {'''I''': 1, '''V''': 5, '''X''': 10, '''L''': 50, '''C''': 100, '''D''': 500, '''M''': 1000}
def UpperCamelCase ( snake_case__):
lowerCAmelCase_ : List[str] = 0
lowerCAmelCase_ : Any = 0
while index < len(snake_case__) - 1:
... | 659 | 0 |
"""simple docstring"""
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_simp... | 65 |
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def UpperCamelCase ( ):
lowerCAmelCase_ : Dict = HfArgumentParser(snake_case__)
lowerCAmelCase_ : Dict = parser.parse_args_into_dataclasses()[0]
lowerCAmelCase_ : Lis... | 659 | 0 |
import unittest
import numpy as np
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
from transformers... | 66 |
from collections import defaultdict
from pathlib import Path
import pandas as pd
from rouge_cli import calculate_rouge_path
from utils import calculate_rouge
_lowercase = [
'''Prosecutor: "No videos were used in the crash investigation" German papers say they saw a cell phone video of the'''
''' ... | 659 | 0 |
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 require_vision
from tra... | 67 |
import json
import os
import unittest
from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property
from ...test_to... | 659 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__A = {
"configuration_llama": ["LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP", "LlamaConfig"],
}
try:
if not i... | 68 |
from ....configuration_utils import PretrainedConfig
from ....utils import logging
_lowercase = logging.get_logger(__name__)
_lowercase = {
'''Visual-Attention-Network/van-base''': (
'''https://huggingface.co/Visual-Attention-Network/van-base/blob/main/config.json'''
),
}
class __... | 659 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable
a : Union[str, Any] = {
'''configuration_gpt_neox_japanese''': ['''GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHI... | 69 |
from math import factorial
def UpperCamelCase ( snake_case__ , snake_case__):
# If either of the conditions are true, the function is being asked
# to calculate a factorial of a negative number, which is not possible
if n < k or k < 0:
raise ValueError("Please enter pos... | 659 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModel,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import AutoencoderKL, DDIMScheduler, D... | 70 |
import argparse
import json
from tqdm import tqdm
def UpperCamelCase ( ):
lowerCAmelCase_ : Any = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--src_path" , type=snake_case__ , default="biencoder-nq-dev.json" , help="Path... | 659 | 0 |
'''simple docstring'''
from maths.prime_check import is_prime
def a__ ( _SCREAMING_SNAKE_CASE : int ) -> int:
"""simple docstring"""
if not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
UpperCAmelCase_ : Any = F'''Input value... | 71 |
from collections.abc import Sequence
def UpperCamelCase ( snake_case__ = None):
if nums is None or not nums:
raise ValueError("Input sequence should not be empty")
lowerCAmelCase_ : Dict = nums[0]
for i in range(1 , len(snake_case__)):
lowerCAme... | 659 | 0 |
'''simple docstring'''
from __future__ import annotations
import bisect
def UpperCamelCase ( lowercase_ : list[int] , lowercase_ : int , lowercase_ : int = 0 , lowercase_ : int = -1 ) -> int:
'''simple docstring'''
if hi < 0:
lowerc... | 72 |
from typing import TYPE_CHECKING
from ....utils import _LazyModule
_lowercase = {'''tokenization_tapex''': ['''TapexTokenizer''']}
if TYPE_CHECKING:
from .tokenization_tapex import TapexTokenizer
else:
import sys
_lowercase = _LazyModule(__name__, globals()['''__file__'''... | 659 | 0 |
from __future__ import annotations
from fractions import Fraction
from math import gcd, sqrt
def lowerCamelCase__ (_UpperCAmelCase):
SCREAMING_SNAKE_CASE = int(number**0.5)
return number == sq * sq
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase , ... | 73 |
import argparse
import glob
import importlib.util
import os
import re
import black
from doc_builder.style_doc import style_docstrings_in_code
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
_lowercase = '''src/diffuse... | 659 | 0 |
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
@dataclass
class __UpperCamelCa... | 74 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase = logging.get_logger(__name__)
_lowercase = {
'''microsoft/swinv2-tiny-patch4-window8-256''': (
'''https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256/resolve/main/config.json'''
... | 659 | 0 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_flava import FlavaImageProcessor
UpperCamelCase__ = logging.get_logger(__name__)
class lowerCamelCase_ ( __a ):
def __init__( self : Union[str, Any] ,... | 75 |
from typing import 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 BatchFeature
from ....file_utils import Paddi... | 659 | 0 |
"""simple docstring"""
import os
import sys
a_ = os.path.join(os.path.dirname(__file__), 'src')
sys.path.append(SRC_DIR)
from transformers import (
AutoConfig,
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForQuestionAnswering,
Au... | 76 |
from __future__ import annotations
# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
_lowercase = 10
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__):
... | 659 | 0 |
"""simple docstring"""
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from transformers import (
VisualBertConfig,
VisualBertForMultipleChoice,
VisualBertForPreTraining,
VisualBertForQuestionAnswering,
VisualBertForVisualReasoning,
)
from transf... | 77 |
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
_lowercase = logging.get_logger(__name__)
_lowercase = {
... | 659 | 0 |
'''simple docstring'''
from itertools import product
from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros
def lowerCAmelCase_ ( snake_case_ : Optional[int] , snake_case_ : List[Any] ) -... | 78 |
from collections.abc import Generator
from math import sin
def UpperCamelCase ( snake_case__):
if len(snake_case__) != 32:
raise ValueError("Input must be of length 32")
lowerCAmelCase_ : Tuple = b""
for i in [3, 2, 1, 0]:
little_endian += string_aa[8... | 659 | 0 |
import argparse
import re
import requests
import torch
# git clone https://github.com/salesforce/BLIP.git
from models.blip import blip_decoder
from models.blip_itm import blip_itm
from models.blip_vqa import blip_vqa
from PIL import Image
from torchvision import transforms
from torchvision.t... | 79 |
import logging
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import librosa
import torch
from datasets import DatasetDict, load_dataset
from packaging import version
from torch import nn
from transformers import (
HfArgumentParser,
Trainer,
Trai... | 659 | 0 |
__UpperCamelCase : List[str] = """Alexander Joslin"""
import operator as op
from .stack import Stack
def snake_case ( lowerCamelCase ):
'''simple docstring'''
__lowercase = {"""*""": op.mul, """/""": op.truediv, """+""": op.add, """-""": op.sub}
__lowercase ... | 80 |
from __future__ import annotations
from collections.abc import Callable
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__ = 1_00 , ):
lowerCAmelCase_ : Any = x_start
lowerCAmelCase_ : Optional[Any] = fnc(snake_case_... | 659 | 0 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_snake_case : Dict = logging.get_logger(__name__)
_snake_case : Dict = {
"facebook/xmod-base": "... | 81 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
PNDMScheduler,
StableDiffusionLDMaDPipeline,
UNetaDConditionModel,
)
from diffusers.utils import nightly, slow, t... | 659 | 0 |
"""simple docstring"""
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Image
from .base import TaskTemplate
@dataclass(frozen=SCREAMING_SNAKE_CASE )
class lowercase__ ( SCREAMING_SNAKE_CASE ):
... | 82 |
import argparse
import re
import numpy as np
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SamConfig,
SamImageProcessor,
SamModel,
SamProcessor,
SamVisionConfig,
)
_lowercase = {
'''iou_prediction_head.lay... | 659 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
... | 83 |
class __snake_case :
"""simple docstring"""
def __init__( self : Union[str, Any] ,lowerCAmelCase__ : str = "" ,lowerCAmelCase__ : bool = False ) -> None:
'''simple docstring'''
lowerCAmelCase_ : dict[str, RadixNode] = {... | 659 | 0 |
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
import sklearn # noqa: F401 # Here to have... | 84 |
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 TFModel... | 659 | 0 |
def _a ( lowercase__ : int = 10_00 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Tuple = 2**power
SCREAMING_SNAKE_CASE__ : Optional[int] = str(lowercase__ )
SCREAMING_SNAKE_CASE__ : int = list(lowercase__ )
SCREAMING_... | 85 |
import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging
_lowerc... | 659 | 0 |
from __future__ import annotations
from typing import Dict
from ...configuration_utils import PretrainedConfig
__a :Dict = {
'susnato/ernie-m-base_pytorch': 'https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json',
'susnato/ernie-m-large_pytorch': 'https://huggingfa... | 86 |
import os
_lowercase = {'''I''': 1, '''V''': 5, '''X''': 10, '''L''': 50, '''C''': 100, '''D''': 500, '''M''': 1000}
def UpperCamelCase ( snake_case__):
lowerCAmelCase_ : List[str] = 0
lowerCAmelCase_ : Any = 0
while index < len(snake_case__) - 1:
... | 659 | 0 |
import json
import os
from collections import Counter
import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset
_lowerCamelCase : Any = {1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1), 6: (3... | 87 |
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def UpperCamelCase ( ):
lowerCAmelCase_ : Dict = HfArgumentParser(snake_case__)
lowerCAmelCase_ : Dict = parser.parse_args_into_dataclasses()[0]
lowerCAmelCase_ : Lis... | 659 | 0 |
"""simple docstring"""
# coding=utf-8
# Copyright 2023 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-... | 88 |
from collections import defaultdict
from pathlib import Path
import pandas as pd
from rouge_cli import calculate_rouge_path
from utils import calculate_rouge
_lowercase = [
'''Prosecutor: "No videos were used in the crash investigation" German papers say they saw a cell phone video of the'''
''' ... | 659 | 0 |
import importlib
import inspect
import os
import re
# 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
SCREAMING_SNAKE_CASE : Optional[Any] = "src/transformers"
# This is to make sure the transformers mod... | 89 |
import json
import os
import unittest
from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property
from ...test_to... | 659 | 0 |
'''simple docstring'''
from bisect import bisect
from itertools import accumulate
def _snake_case ( A , A , A , A ) -> Dict:
lowerCAmelCase__ = sorted(zip(A , A ) , key=lambda A : x[0] / x[1] , reverse... | 90 |
from ....configuration_utils import PretrainedConfig
from ....utils import logging
_lowercase = logging.get_logger(__name__)
_lowercase = {
'''Visual-Attention-Network/van-base''': (
'''https://huggingface.co/Visual-Attention-Network/van-base/blob/main/config.json'''
),
}
class __... | 659 | 0 |
"""simple docstring"""
import itertools
import json
import os
import unittest
from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_token... | 91 |
from math import factorial
def UpperCamelCase ( snake_case__ , snake_case__):
# If either of the conditions are true, the function is being asked
# to calculate a factorial of a negative number, which is not possible
if n < k or k < 0:
raise ValueError("Please enter pos... | 659 | 0 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConformerConfig,
WavaVecaConformerForCTC,
WavaVecaConformerForPreTraining,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
Wava... | 92 |
import argparse
import json
from tqdm import tqdm
def UpperCamelCase ( ):
lowerCAmelCase_ : Any = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--src_path" , type=snake_case__ , default="biencoder-nq-dev.json" , help="Path... | 659 | 0 |
"""simple docstring"""
from tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch
from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER, require_tf, req... | 93 |
from collections.abc import Sequence
def UpperCamelCase ( snake_case__ = None):
if nums is None or not nums:
raise ValueError("Input sequence should not be empty")
lowerCAmelCase_ : Dict = nums[0]
for i in range(1 , len(snake_case__)):
lowerCAme... | 659 | 0 |
'''simple docstring'''
import os
from collections.abc import Iterator
def lowercase_ ( __A : str = "." ) -> Iterator[str]:
"""simple docstring"""
for dir_path, dir_names, filenames in os.walk(__A ):
lowercase : List[Any] =[d for d in dir_names if d... | 94 |
from typing import TYPE_CHECKING
from ....utils import _LazyModule
_lowercase = {'''tokenization_tapex''': ['''TapexTokenizer''']}
if TYPE_CHECKING:
from .tokenization_tapex import TapexTokenizer
else:
import sys
_lowercase = _LazyModule(__name__, globals()['''__file__'''... | 659 | 0 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SwiftFormerConfig,
SwiftFormerForImageClassification,
ViTImageProcessor,
)
from transformers.utils im... | 95 |
import argparse
import glob
import importlib.util
import os
import re
import black
from doc_builder.style_doc import style_docstrings_in_code
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
_lowercase = '''src/diffuse... | 659 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
__lowerCamelCase = {
'configuration_longt5': ['LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LongT5Config', 'LongT5OnnxCo... | 96 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase = logging.get_logger(__name__)
_lowercase = {
'''microsoft/swinv2-tiny-patch4-window8-256''': (
'''https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256/resolve/main/config.json'''
... | 659 | 0 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__a = logging.get_logger(__name__)
__a = {
'xlm-mlm-en-2048': 'https://huggingface.co/xlm-mlm-en-2048/re... | 97 |
from typing import 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 BatchFeature
from ....file_utils import Paddi... | 659 | 0 |
'''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class __lowerCAmelCase ( __magic_name__ ):
"""simple docstring"""
_snake_case : Any = ['image_processor', 'tokenizer'... | 98 |
from __future__ import annotations
# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
_lowercase = 10
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__):
... | 659 | 0 |
import shutil
import tempfile
import unittest
from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio
from .test_feature_extraction_clap import floats_list
@require_torchaudi... | 99 |
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
_lowercase = logging.get_logger(__name__)
_lowercase = {
... | 659 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModel,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, Stabl... | 100 |
from collections.abc import Generator
from math import sin
def UpperCamelCase ( snake_case__):
if len(snake_case__) != 32:
raise ValueError("Input must be of length 32")
lowerCAmelCase_ : Tuple = b""
for i in [3, 2, 1, 0]:
little_endian += string_aa[8... | 659 | 0 |
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_... | 101 |
import logging
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import librosa
import torch
from datasets import DatasetDict, load_dataset
from packaging import version
from torch import nn
from transformers import (
HfArgumentParser,
Trainer,
Trai... | 659 | 0 |
"""simple docstring"""
from __future__ import annotations
__magic_name__ : Any = """Muhammad Umer Farooq"""
__magic_name__ : int = """MIT"""
__magic_name__ : List[Any] = """1.0.0"""
__magic_name__ : Optional[Any] = """Muhammad Um... | 102 |
from __future__ import annotations
from collections.abc import Callable
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__ = 1_00 , ):
lowerCAmelCase_ : Any = x_start
lowerCAmelCase_ : Optional[Any] = fnc(snake_case_... | 659 | 0 |
"""simple docstring"""
import datasets
from .evaluate import evaluate
snake_case = '''\
@article{hendrycks2021cuad,
title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},
author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},
jo... | 103 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
PNDMScheduler,
StableDiffusionLDMaDPipeline,
UNetaDConditionModel,
)
from diffusers.utils import nightly, slow, t... | 659 | 0 |
"""simple docstring"""
def _lowerCamelCase ( UpperCAmelCase_ : int ) -> int:
"""simple docstring"""
if not isinstance(UpperCAmelCase_, UpperCAmelCase_ ):
raise TypeError("only integers accepted as input" )
else:
A__ = ... | 104 |
import argparse
import re
import numpy as np
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SamConfig,
SamImageProcessor,
SamModel,
SamProcessor,
SamVisionConfig,
)
_lowercase = {
'''iou_prediction_head.lay... | 659 | 0 |
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
if is... | 105 |
class __snake_case :
"""simple docstring"""
def __init__( self : Union[str, Any] ,lowerCAmelCase__ : str = "" ,lowerCAmelCase__ : bool = False ) -> None:
'''simple docstring'''
lowerCAmelCase_ : dict[str, RadixNode] = {... | 659 | 0 |
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
__snake_case :Tuple =True
... | 106 |
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 TFModel... | 659 | 0 |
'''simple docstring'''
def _SCREAMING_SNAKE_CASE ( __snake_case : list ):
if len(__snake_case ) <= 1:
return [tuple(__snake_case )]
_A = []
def generate(__snake_case : int , __snake_case : list ):
if k == 1:
r... | 107 |
import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging
_lowerc... | 659 | 0 |
import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import loggi... | 108 |
import os
_lowercase = {'''I''': 1, '''V''': 5, '''X''': 10, '''L''': 50, '''C''': 100, '''D''': 500, '''M''': 1000}
def UpperCamelCase ( snake_case__):
lowerCAmelCase_ : List[str] = 0
lowerCAmelCase_ : Any = 0
while index < len(snake_case__) - 1:
... | 659 | 0 |
'''simple docstring'''
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
execute_subprocess_async,
require_cpu,
require_huggingface_suite,
require_multi_gpu,
require_single_gpu,
)
from accelerate... | 109 |
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def UpperCamelCase ( ):
lowerCAmelCase_ : Dict = HfArgumentParser(snake_case__)
lowerCAmelCase_ : Dict = parser.parse_args_into_dataclasses()[0]
lowerCAmelCase_ : Lis... | 659 | 0 |
from __future__ import annotations
import csv
import requests
from bsa import BeautifulSoup
def __UpperCamelCase ( _A = "" ):
lowerCAmelCase_ = url or "https://www.imdb.com/chart/top/?ref_=nv_mv_250"
lowerCAmelCase_ = BeautifulSoup(requests.get(snake_case__ ).text ... | 431 |
from collections import defaultdict
from pathlib import Path
import pandas as pd
from rouge_cli import calculate_rouge_path
from utils import calculate_rouge
_lowercase = [
'''Prosecutor: "No videos were used in the crash investigation" German papers say they saw a cell phone video of the'''
''' ... | 659 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Callable
UpperCamelCase_ = list[list[float | int]]
def _lowerCAmelCase ( __magic_name__ : Any , __magic_name__ : List[Any] ) -> int:
lowercase : int =... | 92 |
import json
import os
import unittest
from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property
from ...test_to... | 659 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__snake_case = logging.get_logger(__name__)
__snake_case = {
'bigcode/gpt_bigcode-santacoder': 'https://huggingface.co/bigcode/gpt_bigcode-santacoder/resolve/main/config.json',
}
... | 200 |
from ....configuration_utils import PretrainedConfig
from ....utils import logging
_lowercase = logging.get_logger(__name__)
_lowercase = {
'''Visual-Attention-Network/van-base''': (
'''https://huggingface.co/Visual-Attention-Network/van-base/blob/main/config.json'''
),
}
class __... | 659 | 0 |
"""simple docstring"""
import warnings
from pathlib import Path
from typing import List, Tuple, Union
import fire
from torch import nn
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer, PreTrainedModel
from transformers.utils import logging
A_ = logging.get_logger(__name__)
de... | 391 |
from math import factorial
def UpperCamelCase ( snake_case__ , snake_case__):
# If either of the conditions are true, the function is being asked
# to calculate a factorial of a negative number, which is not possible
if n < k or k < 0:
raise ValueError("Please enter pos... | 659 | 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
UpperCAmelCase = WebClient(token=os.environ['CI_SLACK_BOT_TOKEN'])
... | 433 |
import argparse
import json
from tqdm import tqdm
def UpperCamelCase ( ):
lowerCAmelCase_ : Any = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--src_path" , type=snake_case__ , default="biencoder-nq-dev.json" , help="Path... | 659 | 0 |
"""simple docstring"""
import argparse
import OmegaConf
import torch
from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel
def UpperCAmelCase ( _lowercase : Any , _lowercase : List[str] , _lowercase : Any ) -> List[str]:
"""simple docstring"... | 552 |
from collections.abc import Sequence
def UpperCamelCase ( snake_case__ = None):
if nums is None or not nums:
raise ValueError("Input sequence should not be empty")
lowerCAmelCase_ : Dict = nums[0]
for i in range(1 , len(snake_case__)):
lowerCAme... | 659 | 0 |
"""simple docstring"""
import os
from typing import Dict, List, Tuple, TypeVar, Union
__A = TypeVar("""T""")
__A = Union[List[T], Tuple[T, ...]]
__A = Union[T, List[T], Dict[str, T]]
__A = Union[str, bytes, os.PathLike]
| 93 |
from typing import TYPE_CHECKING
from ....utils import _LazyModule
_lowercase = {'''tokenization_tapex''': ['''TapexTokenizer''']}
if TYPE_CHECKING:
from .tokenization_tapex import TapexTokenizer
else:
import sys
_lowercase = _LazyModule(__name__, globals()['''__file__'''... | 659 | 0 |
"""simple docstring"""
import torch
from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde
from .test_schedulers import SchedulerCommonTest
@require_torchsde
class UpperCamelCase__ ( snake_c... | 545 |
import argparse
import glob
import importlib.util
import os
import re
import black
from doc_builder.style_doc import style_docstrings_in_code
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
_lowercase = '''src/diffuse... | 659 | 0 |
'''simple docstring'''
from collections import defaultdict
from pathlib import Path
import pandas as pd
from rouge_cli import calculate_rouge_path
from utils import calculate_rouge
_A = [
"""Prosecutor: \"No videos were used in the crash investigation\" German papers say they saw a cell phone vide... | 158 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase = logging.get_logger(__name__)
_lowercase = {
'''microsoft/swinv2-tiny-patch4-window8-256''': (
'''https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256/resolve/main/config.json'''
... | 659 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ : List[Any] = logging.get_logger(__name__)
UpperCAmelCase_ : Union[str, Any] = {
'unc-nlp/lxmert-base-uncased': 'https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/co... | 570 |
from typing import 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 BatchFeature
from ....file_utils import Paddi... | 659 | 0 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.