code stringlengths 81 54k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
'''simple docstring'''
import argparse
import json
import torch
from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel
def lowercase__ ( __UpperCamelCase , __UpperCamelCase=1 )-> Tuple:
if n_shave_prefix_segments >= 0:
return "... | 705 |
'''simple docstring'''
import argparse
import torch
from torch import nn
from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def lowercase__ ( __UpperCamelCase )-> Any:
UpperCamelCase = [
"""encoder.version""",
... | 35 | 0 |
'''simple docstring'''
import argparse
import gc
import json
import os
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_warm... | 706 |
'''simple docstring'''
import inspect
import unittest
from transformers import MobileNetVaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configu... | 35 | 0 |
'''simple docstring'''
def lowercase__ ( __UpperCamelCase , __UpperCamelCase )-> int:
while b:
UpperCamelCase ,UpperCamelCase = b, a % b
return a
def lowercase__ ( __UpperCamelCase , __UpperCamelCase )-> ... | 707 |
'''simple docstring'''
from typing import Any, Callable, Dict, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
LMSDiscreteScheduler,
PNDM... | 35 | 0 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_r... | 708 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import _LazyModule
SCREAMING_SNAKE_CASE__ = {'tokenization_wav2vec2_phoneme': ['Wav2Vec2PhonemeCTCTokenizer']}
if TYPE_CHECKING:
from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
... | 35 | 0 |
'''simple docstring'''
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
if is_torch_available():
import torch
from transformers import XLMRobertaModel
@re... | 709 |
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = 8.31_44_62 # Unit - J mol-1 K-1
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> float:
if moles < 0 or kelvin < 0 or volume < 0:
raise ValueError("""Invalid ... | 35 | 0 |
'''simple docstring'''
import argparse
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline
if __name__ == "__main__":
SCREAMING_SNAKE_CASE__ = argparse.ArgumentParser()
parse... | 710 |
'''simple docstring'''
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set... | 35 | 0 |
'''simple docstring'''
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoImageProcessor, ViTImageProcessor
from transformers.test... | 711 |
'''simple docstring'''
import collections
import inspect
import unittest
from typing import Dict, List, Tuple
from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_availab... | 35 | 0 |
'''simple docstring'''
import argparse
import os
import subprocess
from packaging.version import Version, parse
from accelerate.commands.config.config_args import default_config_file, load_config_from_file
SCREAMING_SNAKE_CASE__ = 'Run commands across TPU VMs for initial setup before running `... | 712 |
'''simple docstring'''
import importlib.util
import os
import platform
from argparse import ArgumentParser
import huggingface_hub
from .. import __version__ as version
from ..utils import (
is_accelerate_available,
is_flax_available,
is_safetensors_available,
is_tf_availab... | 35 | 0 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
# Register SEW's fairseq modules
from sew_asapp import tasks # noqa: F401
from transformers import (
SEWConfig,
SEWForCTC,
SEWModel,
WavaVecaCTCTokenizer,
... | 713 |
'''simple docstring'''
from math import factorial
def lowercase__ ( __UpperCamelCase = 20 )-> int:
UpperCamelCase = 2 * n # middle entry of odd rows starting at row 3 is the solution for n = 1,
# 2, 3,...
UpperCamelCase ... | 35 | 0 |
'''simple docstring'''
import re
import string
from collections import Counter
import sacrebleu
import sacremoses
from packaging import version
import datasets
SCREAMING_SNAKE_CASE__ = '\n@inproceedings{xu-etal-2016-optimizing,\n title = {Optimizing Statistical Machine T... | 714 |
'''simple docstring'''
from math import sqrt
def lowercase__ ( __UpperCamelCase )-> int:
UpperCamelCase = 0
for i in range(1 , int(sqrt(__UpperCamelCase ) + 1 ) ):
if n % i == 0 and i != sqrt(__UpperCamelCase )... | 35 | 0 |
'''simple docstring'''
import unittest
from knapsack import knapsack as k
class a_ ( unittest.TestCase ):
def A__ ( self ) -> List[str]:
"""simple docstring"""
UpperCamelCase = 0
UpperC... | 715 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
SCREAMING_SNAKE_CASE__ = {
'configuration_swiftformer': [
'SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
... | 35 | 0 |
'''simple docstring'''
from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
SCREAMING_SNAKE_CASE__ = 'facebook/wmt19-en-de'
SCREAMING_SNAKE_CASE__ = FSMTTokenizer.from_pretrained(mname)
# get the correct vocab sizes, etc. from the master model
SCREAMING_S... | 716 |
'''simple docstring'''
def lowercase__ ( __UpperCamelCase )-> str:
if not all(char in """01""" for char in bin_string ):
raise ValueError("""Non-binary value was passed to the function""" )
if not bin_string:
raise ValueError("""Empty str... | 35 | 0 |
'''simple docstring'''
import enum
import os
from hashlib import shaaaa
from typing import Optional
from .. import config
from .logging import get_logger
SCREAMING_SNAKE_CASE__ = get_logger(__name__)
class a_ ( enum.Enum ):
'''simple docstrin... | 717 |
'''simple docstring'''
# Copyright 2022 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/license... | 35 | 0 |
'''simple docstring'''
import darl # noqa
import gym
import tqdm
from diffusers.experimental import ValueGuidedRLPipeline
SCREAMING_SNAKE_CASE__ = {
'n_samples': 6_4,
'horizon': 3_2,
'num_inference_steps': 2_0,
'n_guide_steps': 2, # can set to 0 for faster samplin... | 718 |
'''simple docstring'''
def lowercase__ ( __UpperCamelCase , __UpperCamelCase )-> str:
if not isinstance(__UpperCamelCase , __UpperCamelCase ):
raise ValueError("""iterations must be defined as integers""" )
if not isinstance(__UpperCamelCase , __Up... | 35 | 0 |
import argparse
import math
import traceback
import dateutil.parser as date_parser
import requests
def lowercase__ ( __UpperCamelCase )-> Dict:
UpperCamelCase = {}
UpperCamelCase = job["""started_at"""]
UpperCam... | 719 |
'''simple docstring'''
import argparse
import json
import torch
from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel
def lowercase__ ( __UpperCamelCase , __UpperCamelCase=1 )-> Tuple:
if n_shave_prefix_segments >= 0:
return "... | 35 | 0 |
import collections
import inspect
import unittest
from typing import Dict, List, Tuple
from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_available
from ...test_backbon... | 720 |
'''simple docstring'''
from __future__ import annotations
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> dict[str, float]:
if (voltage, current, resistance).count(0 ) != 1:
raise ValueError("""One and only one argum... | 35 | 0 |
'''simple docstring'''
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_copie... | 721 |
'''simple docstring'''
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny model through reduction of a normal pre-trained model, but keep... | 35 | 0 |
'''simple docstring'''
from cva import destroyAllWindows, imread, imshow, waitKey
def lowercase__ ( __UpperCamelCase )-> Optional[int]:
# getting number of pixels in the image
UpperCamelCase ,UpperCamelCase = img.shape[0], img.shape[1]... | 700 |
'''simple docstring'''
import argparse
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline
if __name__ == "__main__":
SCREAMING_SNAKE_CASE__ = argparse.ArgumentParser()
parse... | 35 | 0 |
'''simple docstring'''
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set... | 701 |
'''simple docstring'''
from __future__ import annotations
from typing import Any
class a_ :
def __init__( self , _SCREAMING_SNAKE_CASE = 6 ) -> None:
"""simple docstring"""
UpperCamelCase = None
Upp... | 35 | 0 |
'''simple docstring'''
import contextlib
from multiprocessing import Pool, RLock
from tqdm.auto import tqdm
from ..utils import experimental, logging
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
class a_ :
lowercase = None
@ex... | 702 |
'''simple docstring'''
import fire
from utils import calculate_rouge, save_json
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase=None , **__UpperCamelCase )-> int:
UpperCamelCase = [x.strip() for x in open(__UpperCame... | 35 | 0 |
from argparse import ArgumentParser
from .env import EnvironmentCommand
def lowercase__ ( )-> Any:
UpperCamelCase = ArgumentParser("""Diffusers CLI tool""" , usage="""diffusers-cli <command> [<args>]""" )
UpperCamelCase = ... | 703 |
'''simple docstring'''
from __future__ import annotations
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> tuple[float, list[float]]:
UpperCamelCase = list(range(len(__UpperCamelCase ) ) )
UpperCame... | 35 | 0 |
'''simple docstring'''
import gc
import unittest
from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import... | 704 |
'''simple docstring'''
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ = {
'SenseTime/deformable-detr... | 35 | 0 |
'''simple docstring'''
import inspect
import unittest
from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_d... | 705 |
'''simple docstring'''
import argparse
import torch
from torch import nn
from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def lowercase__ ( __UpperCamelCase )-> Any:
UpperCamelCase = [
"""encoder.version""",
... | 35 | 0 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class a_ ( metaclass=lowerCamelCase ):
lowercase = ["""speech"""]
def __init__( self , *_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE ) -> Tuple:
"... | 706 |
'''simple docstring'''
import inspect
import unittest
from transformers import MobileNetVaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configu... | 35 | 0 |
'''simple docstring'''
from sklearn.metrics import recall_score
import datasets
SCREAMING_SNAKE_CASE__ = '\nRecall is the fraction of the positive examples that were correctly labeled by the model as positive. It can be computed with the equation:\nRecall = TP / (TP + FN)\nWhere T... | 707 |
'''simple docstring'''
from typing import Any, Callable, Dict, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
LMSDiscreteScheduler,
PNDM... | 35 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
SCREAMING_SNAKE_CASE__ = {
'configuration_blip': [
'BLIP_... | 708 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import _LazyModule
SCREAMING_SNAKE_CASE__ = {'tokenization_wav2vec2_phoneme': ['Wav2Vec2PhonemeCTCTokenizer']}
if TYPE_CHECKING:
from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
... | 35 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE__ ... | 709 |
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = 8.31_44_62 # Unit - J mol-1 K-1
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> float:
if moles < 0 or kelvin < 0 or volume < 0:
raise ValueError("""Invalid ... | 35 | 0 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_deformable_detr import DeformableDetrImageProcessor
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
class a_ ( lowerCamelCase ):
def __init__( sel... | 710 |
'''simple docstring'''
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set... | 35 | 0 |
'''simple docstring'''
class a_ :
def __init__( self , _SCREAMING_SNAKE_CASE ) -> List[Any]:
"""simple docstring"""
UpperCamelCase = arr.split(""",""" )
def A__ ( self ) -> Optional[int]:
... | 711 |
'''simple docstring'''
import collections
import inspect
import unittest
from typing import Dict, List, Tuple
from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_availab... | 35 | 0 |
'''simple docstring'''
def lowercase__ ( __UpperCamelCase , __UpperCamelCase )-> str:
if a < 0 or b < 0:
raise ValueError("""the value of both inputs must be positive""" )
UpperCamelCase = str(bin(__UpperCamelCase ) )[2:] # remove the lead... | 712 |
'''simple docstring'''
import importlib.util
import os
import platform
from argparse import ArgumentParser
import huggingface_hub
from .. import __version__ as version
from ..utils import (
is_accelerate_available,
is_flax_available,
is_safetensors_available,
is_tf_availab... | 35 | 0 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING, TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
from transformers.pipelines import AudioClassificationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
ne... | 713 |
'''simple docstring'''
from math import factorial
def lowercase__ ( __UpperCamelCase = 20 )-> int:
UpperCamelCase = 2 * n # middle entry of odd rows starting at row 3 is the solution for n = 1,
# 2, 3,...
UpperCamelCase ... | 35 | 0 |
'''simple docstring'''
from transformers import BertTokenizerFast
from .custom_tokenization import CustomTokenizer
class a_ ( lowerCamelCase ):
lowercase = CustomTokenizer
pass
| 714 |
'''simple docstring'''
from math import sqrt
def lowercase__ ( __UpperCamelCase )-> int:
UpperCamelCase = 0
for i in range(1 , int(sqrt(__UpperCamelCase ) + 1 ) ):
if n % i == 0 and i != sqrt(__UpperCamelCase )... | 35 | 0 |
'''simple docstring'''
import numpy as np
class a_ :
def __init__( self , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None ) -> Optional[Any]:
... | 715 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
SCREAMING_SNAKE_CASE__ = {
'configuration_swiftformer': [
'SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
... | 35 | 0 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from... | 716 |
'''simple docstring'''
def lowercase__ ( __UpperCamelCase )-> str:
if not all(char in """01""" for char in bin_string ):
raise ValueError("""Non-binary value was passed to the function""" )
if not bin_string:
raise ValueError("""Empty str... | 35 | 0 |
'''simple docstring'''
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> int:
if exponent == 1:
return base
if exponent % 2 == 0:
UpperCamelCase = _modexpt(__UpperCamelCase , exponent // 2 , __Upp... | 717 |
'''simple docstring'''
# Copyright 2022 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/license... | 35 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
SCREAMING_SNAKE_CASE__ = {
'configuration_swiftformer': [
'SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
... | 718 |
'''simple docstring'''
def lowercase__ ( __UpperCamelCase , __UpperCamelCase )-> str:
if not isinstance(__UpperCamelCase , __UpperCamelCase ):
raise ValueError("""iterations must be defined as integers""" )
if not isinstance(__UpperCamelCase , __Up... | 35 | 0 |
import argparse
from torch import nn
# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
ProphetNetForConditionalG... | 719 |
'''simple docstring'''
import argparse
import json
import torch
from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel
def lowercase__ ( __UpperCamelCase , __UpperCamelCase=1 )-> Tuple:
if n_shave_prefix_segments >= 0:
return "... | 35 | 0 |
from __future__ import annotations
def lowercase__ ( __UpperCamelCase )-> None:
create_state_space_tree(__UpperCamelCase , [] , 0 , [0 for i in range(len(__UpperCamelCase ) )] )
def lowercase__ ( __UpperCamelCase , __UpperCame... | 720 |
'''simple docstring'''
from __future__ import annotations
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> dict[str, float]:
if (voltage, current, resistance).count(0 ) != 1:
raise ValueError("""One and only one argum... | 35 | 0 |
'''simple docstring'''
from unittest.mock import patch
import pyspark
from datasets.packaged_modules.spark.spark import (
Spark,
SparkExamplesIterable,
_generate_iterable_examples,
)
from ..utils import (
require_dill_gt_0_3_2,
require_not_windows,
)
def ... | 721 |
'''simple docstring'''
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny model through reduction of a normal pre-trained model, but keep... | 35 | 0 |
"""simple docstring"""
from .imports import is_rich_available
if is_rich_available():
from rich.traceback import install
install(show_locals=False)
else:
raise ModuleNotFoundError("""To use the rich extension, install rich with `pip install rich`""")
| 36 |
"""simple docstring"""
from collections import Counter
from pathlib import Path
from typing import Optional, Tuple
import yaml
class a ( yaml.SafeLoader ):
def lowerCamelCase__ ( self : int , lowerCAmelCase : List[str] ) -> Optional[Any]:
... | 36 | 1 |
"""simple docstring"""
import json
import os
import unittest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
... | 36 |
"""simple docstring"""
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
def __magic_name__ ( lowe... | 36 | 1 |
"""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 ( unittest... | 36 |
"""simple docstring"""
from typing import Dict
import numpy as np
import torch
from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map
def __magic_name__ ( lowercase ):
SCREAMING_SNAKE_CASE_: Optional[Any] =[]
SCREAMING_SNAKE_CAS... | 36 | 1 |
"""simple docstring"""
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 = {
"""facebook/c... | 36 |
"""simple docstring"""
from pathlib import Path
from typing import List
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText
... | 36 | 1 |
"""simple docstring"""
from math import factorial
_UpperCAmelCase = {str(d): factorial(d) for d in range(1_0)}
def __magic_name__ ( lowercase ):
return sum(DIGIT_FACTORIAL[d] for d in str(lowercase ) )
def __magic_name__ ( ):
SCREAMING_SNAKE_CA... | 36 |
"""simple docstring"""
from __future__ import annotations
def __magic_name__ ( lowercase , lowercase ):
SCREAMING_SNAKE_CASE_: List[Any] =sorted(numsa + numsa )
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_: Tuple =divmod(len(lowercase ) , 2 )
... | 36 | 1 |
"""simple docstring"""
import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig
... | 36 |
"""simple docstring"""
import copy
import inspect
import unittest
from transformers import PretrainedConfig, SwiftFormerConfig
from transformers.testing_utils import (
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_avail... | 36 | 1 |
"""simple docstring"""
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
_UpperCAmelCase = {1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2... | 36 |
"""simple docstring"""
from math import pi
def __magic_name__ ( lowercase , lowercase ):
return 2 * pi * radius * (angle / 360)
if __name__ == "__main__":
print(arc_length(9_0, 1_0))
| 36 | 1 |
"""simple docstring"""
from __future__ import annotations
import collections
import pprint
from pathlib import Path
def __magic_name__ ( lowercase ):
return "".join(sorted(lowercase ) )
def __magic_name__ ( lowercase ):
return word_by_signature[sig... | 36 |
"""simple docstring"""
import gc
import unittest
from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy a... | 36 | 1 |
"""simple docstring"""
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_UpperCAmelCase = logging.ge... | 36 |
"""simple docstring"""
def __magic_name__ ( lowercase = 200_0000 ):
SCREAMING_SNAKE_CASE_: List[Any] =[0 for i in range(n + 1 )]
SCREAMING_SNAKE_CASE_: Union[str, Any] =1
SCREAMING_SNAKE_CASE_: Optional[Any] =1
for i in range(2 , int(n**0.5 ) + 1 ):
... | 36 | 1 |
"""simple docstring"""
import argparse
import logging
import sys
from unittest.mock import patch
import run_glue_deebert
from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow
logging.basicConfig(level=logging.DEBUG)
_UpperCAmelCase = loggin... | 36 |
"""simple docstring"""
import argparse
import torch
from transformers import BertForMaskedLM
if __name__ == "__main__":
_UpperCAmelCase = argparse.ArgumentParser(
description=(
"""Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Lear... | 36 | 1 |
"""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=UpperCAmelCase__ )
class a ( UpperCAmelCase__ ):
UpperCamelCase : str... | 36 |
"""simple docstring"""
def __magic_name__ ( lowercase , lowercase ):
return int((input_a, input_a).count(0 ) == 0 )
def __magic_name__ ( ):
assert and_gate(0 , 0 ) == 0
assert and_gate(0 , 1 ) == 0
assert and_gate(1 , 0 ) ==... | 36 | 1 |
"""simple docstring"""
from __future__ import annotations
import json
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
_UpperCAmelCase = {"""UserAgent""": UserAgent().random}
def __magic_name__ ( lowercase ):
SCREAMING_SNAKE_CASE_:... | 36 |
"""simple docstring"""
import argparse
import numpy as np
import torch
from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging
logging.set_verbosity_info()
_UpperCAmelCase = logging.get_logger("""transformers.models.speecht5""")
def __magic_name__ ( ... | 36 | 1 |
"""simple docstring"""
import warnings
from diffusers import StableDiffusionImgaImgPipeline # noqa F401
warnings.warn(
"""The `image_to_image.py` script is outdated. Please use directly `from diffusers import"""
""" StableDiffusionImg2ImgPipeline` instead."""
)
| 36 |
"""simple docstring"""
import argparse
import requests
import torch
from PIL import Image
from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor
def __magic_name__ ( lowercase ):
if "cls_token" in name:
SCREAMING_SNAKE_CASE_: Optional[int] ... | 36 | 1 |
"""simple docstring"""
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from dif... | 36 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
_UpperCAmelCase = {
"""configuration_xlm""": ["""XLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLMConfig""", """XLMOnnxConfig"""],
"""... | 36 | 1 |
"""simple docstring"""
# Usage:
# ./gen-card-facebook-wmt19.py
import os
from pathlib import Path
def __magic_name__ ( lowercase , lowercase , lowercase ):
SCREAMING_SNAKE_CASE_: Tuple ={
"""en""": """Machine learning is great, isn't it?""",
... | 36 |
"""simple docstring"""
def __magic_name__ ( lowercase ):
SCREAMING_SNAKE_CASE_: int =False
while is_sorted is False: # Until all the indices are traversed keep looping
SCREAMING_SNAKE_CASE_: Tuple =True
for i in range(0 , len(lowercase ) - 1 , ... | 36 | 1 |
"""simple docstring"""
import argparse
import pytorch_lightning as pl
import torch
from torch import nn
from transformers import LongformerForQuestionAnswering, LongformerModel
class a ( pl.LightningModule ):
def __init__( self : List[Any] , lowerCAmelCase : Li... | 36 |
"""simple docstring"""
def __magic_name__ ( lowercase ):
return str(lowercase ) == str(lowercase )[::-1]
def __magic_name__ ( lowercase ):
return int(lowercase ) + int(str(lowercase )[::-1] )
def __magic_name__ ( lowercase = 1_0000 ):
... | 36 | 1 |
"""simple docstring"""
import argparse
import os
from pathlib import Path
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegas... | 36 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
_UpperCAmelCase = {"""configuration_dpt""": ["""DPT_PRETRAINED_CONFIG_ARCHIVE_MAP"""... | 36 | 1 |
"""simple docstring"""
import os
import sys
_UpperCAmelCase = os.path.join(os.path.dirname(__file__), """src""")
sys.path.append(SRC_DIR)
from transformers import (
AutoConfig,
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForQuestionAnswering,
Au... | 36 |
"""simple docstring"""
from __future__ import annotations
import math
import random
from typing import Any
class a :
def __init__( self : str ) -> None:
'''simple docstring'''
SCREAMING_SNAKE_CASE_: list[Any] =[]
SCREAMING_SNAKE_CASE_: ... | 36 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
_UpperCAmelCase = {"""configuration_dpt""": ["""DPT_PRETRAINED_CONFIG_ARCHIVE_MAP"""... | 36 |
"""simple docstring"""
import string
def __magic_name__ ( lowercase ):
SCREAMING_SNAKE_CASE_: List[Any] =""""""
for i in sequence:
SCREAMING_SNAKE_CASE_: List[Any] =ord(lowercase )
if 65 <= extract <= 90:
output += chr(155 - extract )
... | 36 | 1 |
"""simple docstring"""
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ConvNextConfig, SegformerImageProcessor, UperNetConfig, UperNetForSemanticSegmentation
def __magic_name__ ( ... | 36 |
"""simple docstring"""
import numpy as np
import torch
from torch.utils.data import DataLoader
from accelerate.utils.dataclasses import DistributedType
class a :
def __init__( self : Union[str, Any] , lowerCAmelCase : List[str]=2 , lowerCAmelCase : int=3 ... | 36 | 1 |
"""simple docstring"""
import warnings
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import D... | 36 |
"""simple docstring"""
def __magic_name__ ( lowercase ):
if upper_limit < 0:
raise ValueError("""Limit for the Catalan sequence must be ≥ 0""" )
SCREAMING_SNAKE_CASE_: Tuple =[0] * (upper_limit + 1)
# Base case: C(0) = C(1) = 1
SCREAMING_SNAKE_CASE_: Any =... | 36 | 1 |
"""simple docstring"""
from manim import *
class a ( UpperCAmelCase__ ):
def lowerCamelCase__ ( self : Union[str, Any] ) -> List[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE_: Union[str, Any] =Rectangle(height=0.5 , width... | 36 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
_UpperCAmelCase = {
"""albert-base-v1""": """https://huggingface.co/albert-base-v1/resolve/main/config.json""",
"""a... | 36 | 1 |
"""simple docstring"""
import argparse
import numpy as np
import torch
from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging
logging.set_verbosity_info()
_UpperCAmelCase = logging.get_logger("""transformers.models.speecht5""")
def __magic_name__ ( ... | 36 |
"""simple docstring"""
from collections import Counter
from pathlib import Path
from typing import Optional, Tuple
import yaml
class a ( yaml.SafeLoader ):
def lowerCamelCase__ ( self : int , lowerCAmelCase : List[str] ) -> Optional[Any]:
... | 36 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import _LazyModule
_UpperCAmelCase = {"""tokenization_bertweet""": ["""BertweetTokenizer"""]}
if TYPE_CHECKING:
from .tokenization_bertweet import BertweetTokenizer
else:
import sys
_UpperCAmelCase = ... | 36 |
"""simple docstring"""
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
def __magic_name__ ( lowe... | 36 | 1 |
"""simple docstring"""
import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class ... | 36 |
"""simple docstring"""
from typing import Dict
import numpy as np
import torch
from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map
def __magic_name__ ( lowercase ):
SCREAMING_SNAKE_CASE_: Optional[Any] =[]
SCREAMING_SNAKE_CAS... | 36 | 1 |
"""simple docstring"""
def __magic_name__ ( lowercase ):
return str(lowercase ) == str(lowercase )[::-1]
def __magic_name__ ( lowercase ):
return int(lowercase ) + int(str(lowercase )[::-1] )
def __magic_name__ ( lowercase = 1_0000 ):
... | 36 |
"""simple docstring"""
from pathlib import Path
from typing import List
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText
... | 36 | 1 |
"""simple docstring"""
_UpperCAmelCase = {}
def __magic_name__ ( lowercase , lowercase , lowercase ):
# if we are absent twice, or late 3 consecutive days,
# no further prize strings are possible
if late == 3 or absent == 2:
return 0
# i... | 36 |
"""simple docstring"""
from __future__ import annotations
def __magic_name__ ( lowercase , lowercase ):
SCREAMING_SNAKE_CASE_: List[Any] =sorted(numsa + numsa )
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_: Tuple =divmod(len(lowercase ) , 2 )
... | 36 | 1 |
"""simple docstring"""
from argparse import ArgumentParser
from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand
_UpperCAmelCase = logging.get_logger(__name__) # pylint: disable=invalid-name
... | 36 |
"""simple docstring"""
import copy
import inspect
import unittest
from transformers import PretrainedConfig, SwiftFormerConfig
from transformers.testing_utils import (
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_avail... | 36 | 1 |
"""simple docstring"""
import math
import random
def __magic_name__ ( lowercase , lowercase = False ):
if deriv:
return value * (1 - value)
return 1 / (1 + math.exp(-value ))
# Initial Value
_UpperCAmelCase = 0.0_2
def __magic_name__ ... | 36 |
"""simple docstring"""
from math import pi
def __magic_name__ ( lowercase , lowercase ):
return 2 * pi * radius * (angle / 360)
if __name__ == "__main__":
print(arc_length(9_0, 1_0))
| 36 | 1 |
"""simple docstring"""
import math
from typing import Optional
import numpy as np
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase = logging.get_logger(__name__)
_UpperCAmelCase = {
"""facebook/encodec_24khz""": """https://hugging... | 36 |
"""simple docstring"""
import gc
import unittest
from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy a... | 36 | 1 |
"""simple docstring"""
import inspect
import unittest
class a ( unittest.TestCase ):
def lowerCamelCase__ ( self : Any ) -> Any:
'''simple docstring'''
try:
import diffusers # noqa: F401
except ImportError:
assert ... | 36 |
"""simple docstring"""
def __magic_name__ ( lowercase = 200_0000 ):
SCREAMING_SNAKE_CASE_: List[Any] =[0 for i in range(n + 1 )]
SCREAMING_SNAKE_CASE_: Union[str, Any] =1
SCREAMING_SNAKE_CASE_: Optional[Any] =1
for i in range(2 , int(n**0.5 ) + 1 ):
... | 36 | 1 |
"""simple docstring"""
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class a ( UpperCAmelCase__ , UpperCAmelCase__ ):
@register_to_config
def __init__( self : Dict , *,
... | 36 |
"""simple docstring"""
import argparse
import torch
from transformers import BertForMaskedLM
if __name__ == "__main__":
_UpperCAmelCase = argparse.ArgumentParser(
description=(
"""Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Lear... | 36 | 1 |
"""simple docstring"""
import unittest
from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch
class a ( unittest.TestCase ):
UpperCamelCase : List[str] = JukeboxTokenizer
UpperCamelCase : Union[str, Any] = {
... | 36 |
"""simple docstring"""
def __magic_name__ ( lowercase , lowercase ):
return int((input_a, input_a).count(0 ) == 0 )
def __magic_name__ ( ):
assert and_gate(0 , 0 ) == 0
assert and_gate(0 , 1 ) == 0
assert and_gate(1 , 0 ) ==... | 36 | 1 |
"""simple docstring"""
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase = logging.get_logger(__name__)
_UpperCAmelCase = {
"""xlnet-base-cased""": """https://huggingface.co/xlnet-base-cased/resolve/main/config.json""",... | 36 |
"""simple docstring"""
import argparse
import numpy as np
import torch
from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging
logging.set_verbosity_info()
_UpperCAmelCase = logging.get_logger("""transformers.models.speecht5""")
def __magic_name__ ( ... | 36 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
_UpperCAmelCase = {
"""confi... | 36 |
"""simple docstring"""
import argparse
import requests
import torch
from PIL import Image
from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor
def __magic_name__ ( lowercase ):
if "cls_token" in name:
SCREAMING_SNAKE_CASE_: Optional[int] ... | 36 | 1 |
"""simple docstring"""
import contextlib
import os
import sqlitea
import pytest
from datasets import Dataset, Features, Value
from datasets.io.sql import SqlDatasetReader, SqlDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases, require_sqlalchemy
... | 36 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
_UpperCAmelCase = {
"""configuration_xlm""": ["""XLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLMConfig""", """XLMOnnxConfig"""],
"""... | 36 | 1 |
"""simple docstring"""
from .constants import (
MODEL_NAME,
OPTIMIZER_NAME,
RNG_STATE_NAME,
SAFE_WEIGHTS_INDEX_NAME,
SAFE_WEIGHTS_NAME,
SCALER_NAME,
SCHEDULER_NAME,
TORCH_LAUNCH_PARAMS,
WEIGHTS_INDEX_NAME,
WEIGHTS_NAME,
)
from .dataclasses import (
BnbQu... | 36 |
"""simple docstring"""
def __magic_name__ ( lowercase ):
SCREAMING_SNAKE_CASE_: int =False
while is_sorted is False: # Until all the indices are traversed keep looping
SCREAMING_SNAKE_CASE_: Tuple =True
for i in range(0 , len(lowercase ) - 1 , ... | 36 | 1 |
"""simple docstring"""
class a :
def __init__( self : Tuple ) -> None:
'''simple docstring'''
SCREAMING_SNAKE_CASE_: dict[str, TrieNode] ={} # Mapping from char to TrieNode
SCREAMING_SNAKE_CASE_: List[Any] =False
def lowerCa... | 36 |
"""simple docstring"""
def __magic_name__ ( lowercase ):
return str(lowercase ) == str(lowercase )[::-1]
def __magic_name__ ( lowercase ):
return int(lowercase ) + int(str(lowercase )[::-1] )
def __magic_name__ ( lowercase = 1_0000 ):
... | 36 | 1 |
"""simple docstring"""
import numpy as np
import torch
from torch.utils.data import DataLoader
from accelerate.utils.dataclasses import DistributedType
class a :
def __init__( self : Union[str, Any] , lowerCAmelCase : List[str]=2 , lowerCAmelCase : int=3 ... | 36 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
_UpperCAmelCase = {"""configuration_dpt""": ["""DPT_PRETRAINED_CONFIG_ARCHIVE_MAP"""... | 36 | 1 |
"""simple docstring"""
def __magic_name__ ( lowercase ):
if isinstance(lowercase , lowercase ):
raise TypeError("""'float' object cannot be interpreted as an integer""" )
if isinstance(lowercase , lowercase ):
raise TypeError("""'str' object cannot be inter... | 36 |
"""simple docstring"""
from __future__ import annotations
import math
import random
from typing import Any
class a :
def __init__( self : str ) -> None:
'''simple docstring'''
SCREAMING_SNAKE_CASE_: list[Any] =[]
SCREAMING_SNAKE_CASE_: ... | 36 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase = logging.get_logger(__name__)
class a ( UpperCAmelCase__ ):
UpperCamelCase : List[Any] = 'timm_backbone'
def __init__( self : T... | 36 |
"""simple docstring"""
import string
def __magic_name__ ( lowercase ):
SCREAMING_SNAKE_CASE_: List[Any] =""""""
for i in sequence:
SCREAMING_SNAKE_CASE_: List[Any] =ord(lowercase )
if 65 <= extract <= 90:
output += chr(155 - extract )
... | 36 | 1 |
"""simple docstring"""
from __future__ import annotations
from math import gcd
def __magic_name__ ( lowercase , lowercase = 2 , lowercase = 1 , lowercase = 3 , ):
# A value less than 2 can cause an infinite loop in the algorithm.
if num < 2:
... | 36 |
"""simple docstring"""
import numpy as np
import torch
from torch.utils.data import DataLoader
from accelerate.utils.dataclasses import DistributedType
class a :
def __init__( self : Union[str, Any] , lowerCAmelCase : List[str]=2 , lowerCAmelCase : int=3 ... | 36 | 1 |
"""simple docstring"""
def __magic_name__ ( lowercase , lowercase ):
return int((input_a, input_a).count(0 ) == 0 )
def __magic_name__ ( ):
assert and_gate(0 , 0 ) == 0
assert and_gate(0 , 1 ) == 0
assert and_gate(1 , 0 ) ==... | 36 |
"""simple docstring"""
def __magic_name__ ( lowercase ):
if upper_limit < 0:
raise ValueError("""Limit for the Catalan sequence must be ≥ 0""" )
SCREAMING_SNAKE_CASE_: Tuple =[0] * (upper_limit + 1)
# Base case: C(0) = C(1) = 1
SCREAMING_SNAKE_CASE_: Any =... | 36 | 1 |
"""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 timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform... | 36 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
_UpperCAmelCase = {
"""albert-base-v1""": """https://huggingface.co/albert-base-v1/resolve/main/config.json""",
"""a... | 36 | 1 |
"""simple docstring"""
import os
import pytest
from attr import dataclass
_UpperCAmelCase = """us-east-1""" # defaults region
@dataclass
class a :
UpperCamelCase : str
UpperCamelCase : List[Any] = 'arn:aws:iam::558105141721:role/sagemaker_execution_role'
Uppe... | 36 |
"""simple docstring"""
from collections import Counter
from pathlib import Path
from typing import Optional, Tuple
import yaml
class a ( yaml.SafeLoader ):
def lowerCamelCase__ ( self : int , lowerCAmelCase : List[str] ) -> Optional[Any]:
... | 36 | 1 |
"""simple docstring"""
import inspect
import logging
import os
import random
import shutil
import tempfile
import unittest
import pytest
import torch
from torch import nn
from torch.utils.data import DataLoader, TensorDataset
from accelerate import Accelerator
from accelerate.test_utils import ... | 36 |
"""simple docstring"""
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
def __magic_name__ ( lowe... | 36 | 1 |
"""simple docstring"""
import timeit
import numpy as np
import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD
def __magic_name__ ( lowercase ):
def wrapper(*lowercase , **lowercase ):
SCREAMING_SNAKE_CAS... | 36 |
"""simple docstring"""
from typing import Dict
import numpy as np
import torch
from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map
def __magic_name__ ( lowercase ):
SCREAMING_SNAKE_CASE_: Optional[Any] =[]
SCREAMING_SNAKE_CAS... | 36 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
_UpperCAmelCase = {
"""albert-base-v1""": """https://huggingface.co/albert-base-v1/resolve/main/config.json""",
"""a... | 36 |
"""simple docstring"""
from pathlib import Path
from typing import List
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText
... | 36 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_UpperCAmelCase = {
"""configuration_roformer""": ["""... | 36 |
"""simple docstring"""
from __future__ import annotations
def __magic_name__ ( lowercase , lowercase ):
SCREAMING_SNAKE_CASE_: List[Any] =sorted(numsa + numsa )
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_: Tuple =divmod(len(lowercase ) , 2 )
... | 36 | 1 |
"""simple docstring"""
import os
import re
import shutil
import sys
import tempfile
import unittest
import black
_UpperCAmelCase = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))
import check_copies #... | 36 |
"""simple docstring"""
import copy
import inspect
import unittest
from transformers import PretrainedConfig, SwiftFormerConfig
from transformers.testing_utils import (
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_avail... | 36 | 1 |
"""simple docstring"""
from queue import PriorityQueue
from typing import Any
import numpy as np
def __magic_name__ ( lowercase , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase , lowe... | 36 |
"""simple docstring"""
from math import pi
def __magic_name__ ( lowercase , lowercase ):
return 2 * pi * radius * (angle / 360)
if __name__ == "__main__":
print(arc_length(9_0, 1_0))
| 36 | 1 |
"""simple docstring"""
import argparse
import logging
import os
import sys
import numpy as np
import onnxruntime
import torch
from bart_onnx.generation_onnx import BARTBeamSearchGenerator
from bart_onnx.reduce_onnx_size import remove_dup_initializers
import transformers
from transformers import B... | 36 |
"""simple docstring"""
import gc
import unittest
from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy a... | 36 | 1 |
"""simple docstring"""
from . import (
albert,
align,
altclip,
audio_spectrogram_transformer,
auto,
autoformer,
bark,
bart,
barthez,
bartpho,
beit,
bert,
bert_generation,
bert_japanese,
bertweet,
big_bird,
bigbird_pegasus,
... | 36 |
"""simple docstring"""
def __magic_name__ ( lowercase = 200_0000 ):
SCREAMING_SNAKE_CASE_: List[Any] =[0 for i in range(n + 1 )]
SCREAMING_SNAKE_CASE_: Union[str, Any] =1
SCREAMING_SNAKE_CASE_: Optional[Any] =1
for i in range(2 , int(n**0.5 ) + 1 ):
... | 36 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_UpperCAmelCase = {"""configuration_xlnet""": ["""... | 36 |
"""simple docstring"""
import argparse
import torch
from transformers import BertForMaskedLM
if __name__ == "__main__":
_UpperCAmelCase = argparse.ArgumentParser(
description=(
"""Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Lear... | 36 | 1 |
"""simple docstring"""
import argparse
import collections
import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_in... | 36 |
"""simple docstring"""
def __magic_name__ ( lowercase , lowercase ):
return int((input_a, input_a).count(0 ) == 0 )
def __magic_name__ ( ):
assert and_gate(0 , 0 ) == 0
assert and_gate(0 , 1 ) == 0
assert and_gate(1 , 0 ) ==... | 36 | 1 |
"""simple docstring"""
import argparse
import requests
import torch
from PIL import Image
from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor
def __magic_name__ ( lowercase ):
SCREAMING_SNAKE_CASE_: str =SwinConfig(image_size=192 )
... | 36 |
"""simple docstring"""
import argparse
import numpy as np
import torch
from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging
logging.set_verbosity_info()
_UpperCAmelCase = logging.get_logger("""transformers.models.speecht5""")
def __magic_name__ ( ... | 36 | 1 |
"""simple docstring"""
import contextlib
import importlib
import io
import unittest
import transformers
# Try to import everything from transformers to ensure every object can be loaded.
from transformers import * # noqa F406
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_... | 36 |
"""simple docstring"""
import argparse
import requests
import torch
from PIL import Image
from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor
def __magic_name__ ( lowercase ):
if "cls_token" in name:
SCREAMING_SNAKE_CASE_: Optional[int] ... | 36 | 1 |
"""simple docstring"""
# Copyright (c) 2021-, NVIDIA CORPORATION. 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/L... | 36 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
_UpperCAmelCase = {
"""configuration_xlm""": ["""XLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLMConfig""", """XLMOnnxConfig"""],
"""... | 36 | 1 |
"""simple docstring"""
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 acceler... | 36 |
"""simple docstring"""
def __magic_name__ ( lowercase ):
SCREAMING_SNAKE_CASE_: int =False
while is_sorted is False: # Until all the indices are traversed keep looping
SCREAMING_SNAKE_CASE_: Tuple =True
for i in range(0 , len(lowercase ) - 1 , ... | 36 | 1 |
"""simple docstring"""
import os
from itertools import chain
from random import randrange, shuffle
import pytest
from .sola import PokerHand
_UpperCAmelCase = (
"""4S 3H 2C 7S 5H""",
"""9D 8H 2C 6S 7H""",
"""2D 6D 9D TH 7D""",
"""TC 8C 2S JH 6C""",
"""JH 8S TH AH QH"""... | 36 |
"""simple docstring"""
def __magic_name__ ( lowercase ):
return str(lowercase ) == str(lowercase )[::-1]
def __magic_name__ ( lowercase ):
return int(lowercase ) + int(str(lowercase )[::-1] )
def __magic_name__ ( lowercase = 1_0000 ):
... | 36 | 1 |
"""simple docstring"""
from __future__ import annotations
def __magic_name__ ( lowercase , lowercase ):
SCREAMING_SNAKE_CASE_: List[Any] =sorted(numsa + numsa )
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_: Tuple =divmod(len(lowercase ) , 2 )
... | 36 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
_UpperCAmelCase = {"""configuration_dpt""": ["""DPT_PRETRAINED_CONFIG_ARCHIVE_MAP"""... | 36 | 1 |
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