code stringlengths 82 53.2k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
_SCREAMING_SNAKE_CASE = {
"""configuration_data2vec_audio""": ["""DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Da... | 502 |
'''simple docstring'''
from typing import List, Optional, Union
import torch
from transformers import (
XLMRobertaTokenizer,
)
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutpu... | 116 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase : Union[str, Any] = {"""configuration_timm_backbone""": ["""TimmBackboneConfig"""]}
try:
if not is_torch... | 720 |
"""simple docstring"""
from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder
# @@protoc_insertion_point(im... | 168 | 0 |
'''simple docstring'''
import os
def a ( UpperCamelCase_ : str = "matrix.txt" ) -> int:
with open(os.path.join(os.path.dirname(lowercase_ ) , lowercase_ ) ) as in_file:
snake_case__ =in_file.read()
snake_case__ =[[int(lowercase_ ) for cell in ... | 538 |
from typing import List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase = logging.get_logger(__name__)
lowerCAmelCase = {
"""huggingface/autoformer-tourism-monthly""": """https://huggingface.co/huggingface/autoformer-tourism-mont... | 462 | 0 |
import gc
import unittest
from parameterized import parameterized
from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow
if is_flax_available():
import jax
import jax.numpy as jnp
... | 711 |
import enum
import warnings
from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
from ..models.auto.modeling_tf_auto imp... | 684 | 0 |
"""simple docstring"""
import numpy as np
import qiskit
def _UpperCamelCase ( UpperCamelCase = 8 , UpperCamelCase = None ) -> str:
"""simple docstring"""
__UpperCAmelCase : Any = np.random.default_rng(seed=SCREAMING_SNAKE_CASE__ )
... | 77 |
"""simple docstring"""
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class snake_case_ ( a_ ):
__lowerCA... | 237 | 0 |
import inspect
import unittest
import numpy as np
from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor
if is_flax_available(... | 313 |
from typing import Tuple, Union
from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone import TimmBackboneConfig
if ... | 313 | 1 |
"""simple docstring"""
from __future__ import annotations
import time
from collections.abc import Sequence
from random import randint
from matplotlib import pyplot as plt
def lowerCamelCase__ ( _lowerCamelCase : Sequence[float] , _lowerCamelCase ... | 549 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
... | 549 | 1 |
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartaaTokenizer, MBartaaTokenizerFast, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,... | 715 |
import unittest
from transformers import (
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TextaTextGenerationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils import is_torch_availab... | 235 | 0 |
"""simple docstring"""
def UpperCAmelCase ( A : str = 3 , A : int = 7 , A : Union[str, Any] = 100_0000 ):
'''simple docstring'''
_UpperCAmelCase = 0
_UpperCAmelCase = 1
for current_denominator in range(1 , limit + ... | 573 |
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = val
snake_case_ = None
snake_case_ = None
def __lowerCAmelCase ( self , __UpperCamelCase ):... | 187 | 0 |
import time
import warnings
from abc import ABC
from copy import deepcopy
from typing import Optional
import torch
from ..utils import add_start_docstrings, logging
snake_case_ : str = logging.get_logger(__name__)
snake_case_ : Tuple = r"\n Args:\n input_ids... | 169 |
from __future__ import annotations
def A (__A : list[int] ) -> list[int]: # This function is recursive
"""simple docstring"""
UpperCAmelCase_ = len(__A )
# If the array contains only one element, we return it (it's the stop condition of
... | 169 | 1 |
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 |
"""simple docstring"""
import argparse
import math
import os
import torch
from neural_compressor.utils.pytorch import load
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, StableDiffusionPipeline, UNetaDConditionModel
def ... | 346 | 0 |
import unittest
import numpy as np
from transformers import is_flax_available
from transformers.testing_utils import require_flax
from ..test_modeling_flax_common import ids_tensor
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.generation import (
FlaxForcedBOSTokenLo... | 82 | import unittest
import numpy as np
import torch
from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class _UpperCAmelCase ( unittest.TestCase ):
'''sim... | 82 | 1 |
import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel
from ...models.attention import BasicTransformerBlock
from ...utils import logging
UpperCAmelCase_ = logging.get_logger(__name__) # pylint: disable=invalid-name
class __UpperCamelCase ( A_... | 32 |
'''simple docstring'''
import argparse
from collections import defaultdict
import yaml
a : List[Any] = 'docs/source/en/_toctree.yml'
def __magic_name__ ( __UpperCAmelCase ) -> str:
'''simple docstring'''
snake_case_ = defaultdict(__Upp... | 640 | 0 |
import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union
import flax
import jax.numpy as jnp
from ..utils import BaseOutput
lowerCamelCase__ : Optional[Any] = """scheduler_config.json""... | 701 |
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase ) -> int:
snake_case__ = abs(__lowerCAmelCase )
snake_case__ = 0
while n > 0:
res += n % 10
n //= 10
return res
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase ) -> int:
sn... | 208 | 0 |
def snake_case (UpperCAmelCase__ ) -> int:
if n == 1 or not isinstance(UpperCAmelCase__ , UpperCAmelCase__ ):
return 0
elif n == 2:
return 1
else:
UpperCamelCase_: Union[str, Any] = [0, 1]
for i in range(2 , n + 1 ):
... | 57 |
'''simple docstring'''
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#... | 150 | 0 |
import unittest
from transformers import RoFormerTokenizer, RoFormerTokenizerFast
from transformers.testing_utils import require_rjieba, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_rjieba
@require_tokenizers
class lowercase ( a , unittest.... | 647 | import argparse
import logging
from collections import namedtuple
import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer # The authors' implementation
from transformers import BertTokenizer
logging.basicConfig(level=logging.INFO)
_lowerCamelCase : ... | 647 | 1 |
'''simple docstring'''
def _a (lowercase__ : str ) -> list:
"""simple docstring"""
return [
txt[:a] + txt[a].upper() + txt[a + 1 :]
for a in range(len(lowercase__ ) )
if txt[a].isalpha()
]
if __name__ == "__main__":
__import__("do... | 56 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_a : List[str] = logging.get_logger(__name__)
_a : Dict = {
"facebook/timesformer": "https://huggingface.co/facebook/timesformer/resolve/main/config.json",
}
class ... | 56 | 1 |
"""simple docstring"""
import argparse
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt
if __name__ == "__main__":
A__ : Optional[int]= argparse.ArgumentParser()
parser.add_argument(
"""--checkpoint_path""", def... | 20 |
"""simple docstring"""
import argparse
import json
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification
def lowerCAmelCase_( SCRE... | 20 | 1 |
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__ : int = {
"configuration_... | 85 |
import unittest
from knapsack import knapsack as k
class UpperCAmelCase( unittest.TestCase ):
"""simple docstring"""
def __a ( self ) -> Any:
"""simple docstring"""
lowercase__ : Optional[int] = 0
... | 397 | 0 |
"""simple docstring"""
from __future__ import annotations
import csv
import requests
from bsa import BeautifulSoup
def lowercase ( __snake_case : str = "" ):
lowercase_ : List[Any] = url or '''https://www.imdb.com/chart/top/?ref_=nv_mv_250'''
... | 700 |
"""simple docstring"""
from unittest import TestCase
from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters
def lowercase ( ):
lowercase_ : Union[str, Any] = {
'''repo_name''': ['''test_repo1''', ... | 141 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCAmelCase : List[str] = logging.get_logger(__name__)
_lowerCAmelCase : List[str] = {
'''google/vivit-b-16x2-kinetics400''': (
'''https://huggingface.co/go... | 46 |
SCREAMING_SNAKE_CASE__ : Tuple = {
"""a""": """AAAAA""",
"""b""": """AAAAB""",
"""c""": """AAABA""",
"""d""": """AAABB""",
"""e""": """AABAA""",
"""f""": """AABAB""",
"""g""": """AABBA""",
"""h""": """AABBB""",
"""i""": """ABAAA""",
"""j""": """BBBAA""",
"""... | 0 | 0 |
import argparse
import collections
import os
import re
import tempfile
import pandas as pd
from datasets import Dataset
from huggingface_hub import hf_hub_download, upload_folder
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the ro... | 704 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiec... | 450 | 0 |
def _a ( __lowercase ) -> list:
"""simple docstring"""
if len(__lowercase ) <= 1:
return [tuple(__lowercase )]
__UpperCamelCase = []
def generate(__lowercase , __lowercase ):
if k == 1:
res.a... | 383 |
from __future__ import annotations
from collections import Counter
from random import random
class lowerCAmelCase_ :
"""simple docstring"""
def __init__( self ) -> List[str]:
__UpperCamelCase = {}
def __lowercase( self , _SCREAMING_SNAKE_CASE ) ->... | 383 | 1 |
"""simple docstring"""
import argparse
import torch
from transformers import (
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaForAudioFrameClassification,
WavaVecaForSequenceClassification,
WavaVecaForXVector,
logging,
)
logging.set_verbosity_info()
lowercase__ : List[Any] ... | 708 |
"""simple docstring"""
import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize
def __lowercase ( _a ):
return np.dot(_a , _a )
class _UpperCAmelCase :
def __init__( self : int , *,
lowercase_ : float = np.inf , ... | 485 | 0 |
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
import numpy as np
# Parrameters
__snake_case = (7_2_0, 1_2_8_0) # Height, Width
__snake_case = (0.4, 0.6) # if height or width lower than this scale, drop it.
__snake_case ... | 1 |
"""simple docstring"""
import re
from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P
# Sentinels
__A = object()
# For specifying empty leaf dict `{}`
__A = object()
def ... | 346 | 0 |
'''simple docstring'''
import torch
from diffusers import DiffusionPipeline
class _a ( __UpperCAmelCase ):
"""simple docstring"""
def __init__( self ,__SCREAMING_SNAKE_CASE ,__SCREAMING_SNAKE_CASE ):
super().__init__()
sel... | 701 |
'''simple docstring'''
import argparse
from pathlib import Path
import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version
from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import loggi... | 220 | 0 |
'''simple docstring'''
from collections.abc import Callable
class __UpperCamelCase :
def __init__( self :Optional[int] ,_UpperCamelCase :Callable | None = None ):
# Stores actual heap items.
snake_case_ : list = []
# Stores indexes of ea... | 334 |
'''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_simplify, require... | 334 | 1 |
'''simple docstring'''
import os
import time
import numpy as np
import onnxruntime as ort
__UpperCAmelCase = "1"
__UpperCAmelCase = "0"
__UpperCAmelCase = "1"
__UpperCAmelCase = ort.SessionOptions()
__UpperCAmelCase = ort.GraphOptimizationLevel.O... | 692 |
'''simple docstring'''
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, ByTaTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...test_tokenization_common... | 692 | 1 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Iterator
from typing import Any
class __SCREAMING_SNAKE_CASE :
def __init__( self : Optional[int] , UpperCAmelCase__ : Any ):
'''simple docstring'''
lowercase ... | 92 |
from __future__ import annotations
from math import pi
# Define the Reduced Planck Constant ℏ (H bar), speed of light C, value of
# Pi and the function
__magic_name__ = 1.054_571_817E-34 # unit of ℏ : J * s
__magic_name__ = 3E8 # unit of c : m * s^-1
def __magic_name... | 250 | 0 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE_ ( ) -> int:
return 1
def SCREAMING_SNAKE_CASE_ ( snake_case_ : int ) -> int:
return 0 if x < 0 else two_pence(x - 2 ) + one_pence()
def SCREAMING_SNAKE_CASE_ ( snake_case_ : int ... | 220 |
'''simple docstring'''
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
Auto... | 220 | 1 |
"""simple docstring"""
import argparse
import os
import re
import torch
from flax.traverse_util import flatten_dict
from tax import checkpoints
from transformers import (
AutoTokenizer,
PixaStructConfig,
PixaStructForConditionalGeneration,
PixaStructImageProcessor,
... | 573 |
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = val
snake_case_ = None
snake_case_ = None
def __lowerCAmelCase ( self , __UpperCamelCase ):... | 187 | 0 |
from math import isclose, sqrt
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
lowercase = point_y / 4 / point_x
lowercase = 2 * normal_gradient / (1 + normal_gradient * normal_gradient)
lowercase ... | 701 |
from abc import ABC, abstractmethod
from typing import List, Optional
class A_ ( __lowerCamelCase ):
'''simple docstring'''
def __init__( self ):
# test for the above condition
self.test()
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = 0
lowercase = ... | 565 | 0 |
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_ = _LazyModule(__... | 625 |
import inspect
import unittest
from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_config... | 625 | 1 |
import itertools
import random
import unittest
import numpy as np
from transformers import is_speech_available
from transformers.testing_utils import require_torch, require_torchaudio
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
if is_speech_avai... | 703 |
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 YolosConfig, YolosForObjectDetection, YolosImageProcessor
from transformers.utils import logging
logging.set_verbosity... | 369 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaControlnetImgaImgPipeline,
KandinskyVaaPriorEmbaEmbPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tenso... | 39 |
'''simple docstring'''
def lowercase__ ( __UpperCamelCase )-> bool:
UpperCamelCase = (1 + 24 * n) ** 0.5
return ((1 + root) / 6) % 1 == 0
def lowercase__ ( __UpperCamelCase = 5000 )-> int:
UpperCamelCase ... | 301 | 0 |
"""simple docstring"""
def lowerCamelCase__ ( ):
'''simple docstring'''
_lowerCAmelCase : Tuple = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]
_lowerCAmelCase : str = 6
_lowerCAmelCase : Any = 1
_lowerCAmelCase : ... | 16 |
"""simple docstring"""
from __future__ import annotations
import json
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
_lowerCAmelCase = {"""UserAgent""": UserAgent().random}
def lowerCamelCase__ ( _lowerCamelCase ):
'''simple docstr... | 16 | 1 |
def __lowerCAmelCase ( _UpperCamelCase : int = 10 , _UpperCamelCase : int = 10_00 , _UpperCamelCase : bool = True ) -> int:
'''simple docstring'''
assert (
isinstance(_UpperCamelCase , _UpperCamelCase )
and isinstance(_UpperCamelCase , _UpperCamelCase ... | 439 |
import argparse
from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging
logging.set_verbosity_info()
def __lowerCAmelCase ( _UpperCamelCase : str , _UpperCamelCase : Union[str, Any] , ... | 439 | 1 |
'''simple docstring'''
def lowercase ( lowerCAmelCase : Any):
"""simple docstring"""
if n == 1 or not isinstance(a__ , a__):
return 0
elif n == 2:
return 1
else:
_A : List[str] = [0, 1]
for i in range(2 , n + 1):
... | 714 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mvp ... | 417 | 0 |
import unittest
import numpy as np
from transformers import DistilBertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
... | 114 |
"""simple docstring"""
import warnings
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_UpperCAmelCase = logging.get_logger(__name__)... | 409 | 0 |
"""simple docstring"""
import math
import random
from typing import Any
from .hill_climbing import SearchProblem
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase = True , __lowerCAmelCase = math.inf , __lowerCAmelCase = -math.inf , __lowerCAmelCase = math.inf , __lowerCAmel... | 704 |
"""simple docstring"""
from typing import List
import datasets
from datasets.tasks import AudioClassification
from ..folder_based_builder import folder_based_builder
UpperCAmelCase : Union[str, Any] = datasets.utils.logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE__ ( f... | 100 | 0 |
'''simple docstring'''
import os
import tempfile
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from torch import nn
from transformers import (
Adafactor,
... | 22 |
'''simple docstring'''
from typing import Optional, Tuple, Union
import torch
from einops import rearrange, reduce
from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
... | 22 | 1 |
"""simple docstring"""
from __future__ import annotations
def lowerCamelCase_ ( __lowerCAmelCase , __lowerCAmelCase = None ) -> list[list[str]]:
'''simple docstring'''
lowerCamelCase__ =word_bank or []
# create a table
lowerCamelCase__ ... | 132 | """simple docstring"""
import random
import unittest
import numpy as np
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionImgaImgPipeline,
PNDMScheduler,
)
from diffusers.utils import floats_... | 132 | 1 |
import os
import sys
import unittest
_lowerCamelCase : Optional[int] = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))
import get_test_info # noqa: E402
from get_test_info import ( # noqa: E402
get_mode... | 87 |
"""simple docstring"""
import platform
from argparse import ArgumentParser
import huggingface_hub
from .. import __version__ as version
from ..utils import is_accelerate_available, is_torch_available, is_transformers_available, is_xformers_available
from . import BaseDiffusersCLICommand
def lowercase_... | 621 | 0 |
from __future__ import annotations
from random import random
from typing import Generic, TypeVar
lowerCamelCase__ : Optional[Any] = TypeVar("""KT""")
lowerCamelCase__ : str = TypeVar("""VT""")
class _snake_case ( Generic[KT, VT] ):
def __init__( sel... | 495 |
import argparse
import os
import re
lowerCamelCase__ : List[Any] = """src/transformers"""
# Pattern that looks at the indentation in a line.
lowerCamelCase__ : Union[str, Any] = re.compile(R"""^(\s*)\S""")
# Pattern that matches `"key":" and puts `key` in group 0.
lowerCamel... | 495 | 1 |
'''simple docstring'''
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer
class snake_case ( lowercase_ ):
... | 294 |
def __lowerCAmelCase ( __lowerCamelCase : int , __lowerCamelCase : int ) -> bool:
return numa ^ numa < 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 354 | 0 |
import timeit
import numpy as np
import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD
def _SCREAMING_SNAKE_CASE ( snake_case_ : int ):
def wrapper(*snake_case_ : int , **snake_case_ : Optional[Any] ):
__magic_name__ =... | 703 |
from __future__ import annotations
import collections
import pprint
from pathlib import Path
def _SCREAMING_SNAKE_CASE ( snake_case_ : str ):
return "".join(sorted(snake_case_ ) )
def _SCREAMING_SNAKE_CASE ( snake_case_ : str ):
return word_by_signature[signature(snake_case_ )... | 678 | 0 |
import requests
lowercase : List[Any] = """https://newsapi.org/v1/articles?source=bbc-news&sortBy=top&apiKey="""
def UpperCAmelCase_ ( _UpperCAmelCase ):
# fetching a list of articles in json format
lowerCamelCase_: Any = requests.get(_NEWS_API + bbc_new... | 423 |
"""simple docstring"""
def lowerCAmelCase__ ( __magic_name__ ) ->int:
if not isinstance(__magic_name__ , __magic_name__ ):
raise ValueError("multiplicative_persistence() only accepts integral values" )
if num < 0:
raise ValueError("multiplicative_pe... | 118 | 0 |
def A_ ( _lowerCAmelCase , _lowerCAmelCase ) -> int:
return int(input_a == input_a == 0 )
def A_ ( ) -> None:
print("Truth Table of NOR Gate:" )
print("| Input 1 | Input 2 | Output |" )
print(F"""| 0 | 0 | {nor_gate(0 , 0 )} |"""... | 38 |
from __future__ import annotations
import os
import tempfile
import unittest
from transformers import ConvBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tens... | 38 | 1 |
"""simple docstring"""
import csv
import tweepy
# Twitter API credentials
_A = ''
_A = ''
_A = ''
_A = ''
def SCREAMING_SNAKE_CASE ( __UpperCAmelCase ) -> None:
SCREAMING_SNAKE_CASE__ = tweepy.OAuthHandler(__UpperC... | 159 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transform... | 395 | 0 |
"""simple docstring"""
from __future__ import annotations
def UpperCamelCase_ ( SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
if not nums:
return 0
SCREAMING_SNAKE_CASE = nums[0]
SCREAMING_SNAKE_CASE = 0
for num in nums[1... | 706 |
"""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 YolosConfig, YolosForObjectDetection, YolosImageProcessor
from transformers.utils import logging
... | 406 | 0 |
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applica... | 327 |
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applica... | 327 | 1 |
import argparse
import requests
import torch
from PIL import Image
from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def _lowerCAmelCase ( _lowerCAmelCase ):
'''simple docstring'''
if "img_encoder.pos_embed" in name:
A_ : Tuple = name.replace("""img_... | 481 |
_lowerCAmelCase = """ABCDEFGHIJKLMNOPQRSTUVWXYZ"""
def _lowerCAmelCase ( ):
'''simple docstring'''
A_ : Any = input("""Enter message: """ )
A_ : Dict = input("""Enter key [alphanumeric]: """ )
A_ : Dict = input("""Encrypt/Decrypt ... | 481 | 1 |
from math import pi, sqrt, tan
def A__ ( lowerCamelCase ) -> float:
if side_length < 0:
raise ValueError("""surface_area_cube() only accepts non-negative values""" )
return 6 * side_length**2
def A__ ( lowerCamelCase , lowerCamelCase , ... | 548 |
import subprocess
import sys
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
from transformers.testing_utils import TestCasePlus, require_torch
class _UpperCamelCase ( _A ):
'''simple docstring'''
@require_torch
def lowerCAmelCase__ ( self : ... | 548 | 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
def _lowerca... | 12 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
a :List[Any] = logging.get_logger(__name__)
a :Optional[int] = {
"microsoft/focalnet-tiny":... | 12 | 1 |
'''simple docstring'''
from __future__ import annotations
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ ) -> tuple[int, int]:
if b == 0:
return (1, 0)
((UpperCAmelCase__) , (UpperCAmelCase__)) : List[str] = extended_euclid(lowerCAmelCase... | 75 |
def _A ( SCREAMING_SNAKE_CASE : list[int] , SCREAMING_SNAKE_CASE : int ):
"""simple docstring"""
a__ : List[str] =len(SCREAMING_SNAKE_CASE )
a__ : Optional[int] =[[False] * (required_sum + 1) for _ in range(arr_len + 1 )]
# for each arr ... | 563 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Callable
from typing import Any, Generic, TypeVar
lowerCAmelCase = TypeVar("""T""")
class lowerCamelCase ( Generic[T] ):
def __init__( self , a_ , a_ ):
lowerCAmelCase ... | 551 |
'''simple docstring'''
import numpy as np
def __A ( a_ : np.array ):
return (2 / (1 + np.exp(-2 * vector ))) - 1
if __name__ == "__main__":
import doctest
doctest.testmod()
| 551 | 1 |
import inspect
import unittest
from transformers import MobileNetVaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTeste... | 352 |
import unittest
import numpy as np
from transformers import DistilBertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy a... | 352 | 1 |
from importlib import import_module
from .logging import get_logger
snake_case_ : str = get_logger(__name__)
class lowercase__ :
'''simple docstring'''
def __init__( self , lowerCamelCase__ , lowerCamelCase__=None ):
'''simple docstring'''... | 707 |
'''simple docstring'''
import argparse
import json
import os
import re
import torch
from transformers import BloomConfig, BloomModel
from transformers.file_utils import CONFIG_NAME, WEIGHTS_NAME
from transformers.utils import logging
logging.set_verbosity_info()
snake_case_ : Tuple = ... | 350 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase : Tuple = {"configuration_xglm": ["XGLM_PRETRAINE... | 457 |
import itertools
import random
import unittest
import numpy as np
from transformers import is_speech_available
from transformers.testing_utils import require_torch, require_torchaudio
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
if is_speech_available():
from trans... | 457 | 1 |
import math
import random
from typing import Any
from .hill_climbing import SearchProblem
def lowerCAmelCase__ ( _SCREAMING_SNAKE_CASE : str , _SCREAMING_SNAKE_CASE : bool = True , _SCREAMING_SNAKE_CASE : float = math.inf , _SCREAMING_SNAKE_CASE : ... | 718 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowerCamelCase__ = {
"""configuration_nezha""": ["""NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """NezhaConfig"""],
}
try:
if not is_torch_available():
... | 547 | 0 |
'''simple docstring'''
from copy import deepcopy
from typing import Optional, Union
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available
if is_torch_available():
imp... | 38 |
import unittest
from transformers import MraConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_torc... | 332 | 0 |
'''simple docstring'''
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ ):
if a < 0 or b < 0:
raise ValueError("the value of both inputs must be positive" )
lowerCamelCase_ = str(bin(lowerCamelCase__ ) )[2:] # remove the leading "0b"
lowe... | 702 |
from typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, FlaxT... | 313 | 0 |
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_pegasus import DEFAULTS, task_specific_para... | 225 | class __A :
'''simple docstring'''
def __init__( self ):
_lowerCAmelCase : Dict = ""
_lowerCAmelCase : Optional[Any] = ""
_lowerCAmelCase : List[Any] = []
def SCREAMING_SNAKE_CASE__ ( self , _snake_case ... | 424 | 0 |
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging
__magic_name__ = logging.get_logger(__na... | 73 |
import unittest
import numpy as np
from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_avail... | 73 | 1 |
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 = {
'shi-labs/nat-mini-in1k-224': 'https://... | 6 |
import inspect
import os
import unittest
from pathlib import Path
import torch
import accelerate
from accelerate.test_utils import execute_subprocess_async
from accelerate.test_utils.testing import run_command
class __lowerCAmelCase ( unittest.TestCase ):
lowerCamelCase_ : Tuple = inspect.... | 60 | 0 |
from typing import List, Optional, Union
import numpy as np
import tensorflow as tf
from .utils import logging
a : Optional[int] = logging.get_logger(__name__)
def __lowerCamelCase ( _lowercase ) -> Tuple:
if isinstance(SCREAMING_SNAKE_CASE__ , np.n... | 704 |
'''simple docstring'''
a : List[Any] = """Alexander Joslin"""
import operator as op
from .stack import Stack
def __lowerCamelCase ( _lowercase ) -> int:
UpperCAmelCase : Dict = {"""*""": op.mul, """/""": op.truediv, """+""": op.add, """-""": op.sub}
... | 672 | 0 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import t... | 668 |
import os
import tempfile
import unittest
from transformers import DistilBertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_ten... | 668 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase_ : Any = logging.get_logger(__name__)
lowerCAmelCase_ : Optional[Any] = ... | 705 | '''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 accel... | 204 | 0 |
"""simple docstring"""
import argparse
from pathlib import Path
import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version
from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import... | 361 |
"""simple docstring"""
def A_ ( lowercase , lowercase ) -> int:
"""simple docstring"""
return number | (1 << position)
def A_ ( lowercase , lowercase ) -> int:
"""simple docstring"""
return number & ~(1 << position... | 470 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ = {
'''s-JoL/Open-Llama-V1''': '''https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json''',
}
class __A( __lowerCamelCase ):
"""... | 86 |
import argparse
import hashlib
import os
import urllib
import warnings
import torch
from torch import nn
from tqdm import tqdm
from transformers import WhisperConfig, WhisperForConditionalGeneration
lowerCamelCase_ = {
'''tiny.en''': '''https://openaipublic.azureedge.net/main/whisper/models/d3dd57d32accea... | 86 | 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
if TYPE_CHECK... | 497 |
'''simple docstring'''
from __future__ import annotations
def A__ ( A_ , A_ ) -> list[str]:
if nth_term == "":
return [""]
_lowercase = int(A_ )
_lowercase = int(A_ )
_lowercase = []
for temp in range(int(A_ ) ):
series.append(F""... | 497 | 1 |
"""simple docstring"""
import math
class _A :
"""simple docstring"""
def __snake_case ( self : Optional[int] , __UpperCAmelCase : list[list[float]] , __UpperCAmelCase : list[int]):
... | 135 |
"""simple docstring"""
from typing import Dict, List, Optional, Tuple, Union
import torch
from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipel... | 135 | 1 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCamelCase__ = logging.get_logger(__name__)
lowerCamelCase__ = {
'''facebook/deit-ba... | 225 |
# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say tha... | 428 | 0 |
import json
import logging
import os
import re
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import datasets
import numpy as np
import torch
import torchaudio
from packaging import version
from torch import nn
import transformers
from transformers import (
... | 706 |
import argparse
from collections import defaultdict
import yaml
lowercase__ :Optional[int] = "docs/source/en/_toctree.yml"
def UpperCamelCase ( lowerCAmelCase__ ):
'''simple docstring'''
lowercase = defaultdict(lowerCAmelCase__ )
for doc in model_doc:
counts[... | 633 | 0 |
import math
import os
import re
import sys
import unittest
from pathlib import Path
from typing import Tuple
from unittest.mock import patch
from parameterized import parameterized
from transformers.testing_utils import (
CaptureStderr,
ExtendSysPath,
TestCasePlus,
execute_subprocess_async,
... | 385 |
def A ( lowercase__ : int , lowercase__ : int ) -> int:
return int(input_a == input_a == 0 )
def A ( ) -> None:
print("""Truth Table of NOR Gate:""" )
print("""| Input 1 | Input 2 | Output |""" )
print(f"""| 0 | 0 | {nor_gate(0 , 0 )} |""" )
p... | 45 | 0 |
from __future__ import annotations
def lowercase_ ( __snake_case : list[int] ) -> int:
'''simple docstring'''
snake_case__ :Union[str, Any] = len(__snake_case ) // 2
# choose the middle 3 elements
snake_case__ ... | 57 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
__UpperCAmelCase : Tuple = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable... | 57 | 1 |
"""simple docstring"""
import argparse
import json
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_... | 247 |
"""simple docstring"""
from __future__ import annotations
__UpperCamelCase = [-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0]
__UpperCamelCase = [-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1]
def lowercase (SCREAMING_SNAKE_CASE_ : list[float] ) ... | 247 | 1 |
from typing import Dict, List, Optional, Tuple, Union
import torch
from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class __SCREAMING_SNAKE_CASE ... | 710 |
import argparse
import datetime
def _UpperCamelCase (a__ :str ):
"""simple docstring"""
UpperCamelCase__ = {
"""0""": """Sunday""",
"""1""": """Monday""",
"""2""": """Tuesday""",
"""3""": """Wednesday""",
... | 548 | 0 |
"""simple docstring"""
def _snake_case ( _snake_case : int = 10_00 ) -> int:
'''simple docstring'''
_A = 2**power
_A = str(_snake_case )
_A = list(_snake_case )
_A = 0
for i in list_num:
sum_of_num += int(_snake... | 7 |
import torch
from transformers import CamembertForMaskedLM, CamembertTokenizer
def lowerCamelCase_ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase=5 ):
# Adapted from https://github.com/pytorch/fairseq/blob/master/fairseq/models/roberta/hub_interface.p... | 141 | 0 |
"""simple docstring"""
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin... | 239 |
"""simple docstring"""
from math import ceil
def __lowerCAmelCase ( __lowerCAmelCase : int = 1001 ) -> int:
_UpperCamelCase : Tuple = 1
for i in range(1 , int(ceil(n / 2.0 ) ) ):
_UpperCamelCase : Tuple = 2 * i + 1
_UpperCamelCase... | 239 | 1 |
'''simple docstring'''
def a ( __a = 1000000 ) -> int:
'''simple docstring'''
UpperCamelCase__ :str = set(range(3 , snake_case_ , 2 ) )
primes.add(2 )
for p in range(3 , snake_case_ , 2 ):
if p not in primes:
... | 189 |
"""simple docstring"""
import requests
a_ = """https://newsapi.org/v1/articles?source=bbc-news&sortBy=top&apiKey="""
def __lowercase ( snake_case_ : str ) ->None:
'''simple docstring'''
__A : str = requests.get(_NEWS_API + bbc_news_api_key ).json()... | 177 | 0 |
import argparse
import json
from collections import OrderedDict
import torch
from huggingface_hub import cached_download, hf_hub_url
from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification
def lowerCamelCase__ ( snake_case_ : Optional[int] ) ... | 712 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
snake_case_ = {
'configuration_electra': ['ELECTRA_PRETRAINED_CONFIG_AR... | 388 | 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_tor... | 408 |
import argparse
import json
import os
from collections import OrderedDict
import torch
from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def lowerCamelCase__ ( _lowerCamelCase , _lowerCame... | 408 | 1 |
def lowerCAmelCase_ ( __lowerCamelCase , __lowerCamelCase ):
if a < 0 or b < 0:
raise ValueError("the value of both inputs must be positive" )
__snake_case : Tuple = str(bin(lowerCamelCase_ ) )[2:] # remove the leading "0b"
__snake_cas... | 706 |
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
from .record_evaluation import evaluate as evaluate_record
_snake_case : List[Any] = "\\n@article{wang2019superglue,\n title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},\n author={Wan... | 203 | 0 |
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import datasets
import datasets.config
from .utils import require_beam
class lowerCamelCase ( datasets.BeamBasedBuilder ):
'''simple docstring'''
def A__ ( ... | 579 |
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
SCREAMING_SNAKE_CASE = {1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1), 6: (3, 2), 7: (... | 579 | 1 |
import argparse
import pytorch_lightning as pl
import torch
from torch import nn
from transformers import LongformerForQuestionAnswering, LongformerModel
class UpperCamelCase__ ( pl.LightningModule ):
'''simple docstring'''
def __init__( self , UpperCamelCase__ ):
super()... | 705 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tensorflow_text_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_SCREAMING_SNAKE_CASE : int = {
'configuration_bert... | 55 | 0 |
import gzip
import hashlib
import json
import multiprocessing
import os
import re
import shutil
import time
from pathlib import Path
import numpy as np
from arguments import PreprocessingArguments
from datasets import load_dataset
from minhash_deduplication import deduplicate_dataset
from transformers import A... | 68 |
'''simple docstring'''
def lowerCAmelCase (__A , __A):
"""simple docstring"""
if digit_amount > 0:
return round(number - int(__A) , __A)
return number - int(__A)
if __name__ == "__main__":
print(decimal_isolate(1.53, 0))
print(decimal_isolate(35.345, 1))
... | 11 | 0 |
from __future__ import annotations
UpperCAmelCase = 10
def __lowerCAmelCase (SCREAMING_SNAKE_CASE )-> Tuple:
"""simple docstring"""
snake_case_ = 1
snake_case_ = max(__lowerCAmelCase )
while placement <= max_digit:
# declare and initializ... | 718 |
from math import sqrt
import numpy as np
from sympy import symbols
# Coefficient
# Speed of light (m/s)
UpperCAmelCase = 2_9979_2458
# Symbols
UpperCAmelCase, UpperCAmelCase, UpperCAmelCase, UpperCAmelCase = symbols("""ct x y z""")
def __lowerCAmelCase (SCREAMING_SNAKE_C... | 531 | 0 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_av... | 178 |
"""simple docstring"""
from manim import *
class _lowerCAmelCase ( snake_case_ ):
def lowerCamelCase ( self ) -> Any:
'''simple docstring'''
snake_case : List[str] = Rectangle(height=0.5 , width=0.5 )
snake_case ... | 178 | 1 |
'''simple docstring'''
from typing import Any
class _UpperCamelCase :
'''simple docstring'''
def __init__( self : List[str] , lowerCAmelCase__ : Any ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : List[Any] = data
__SCREAMING_SNAKE_CA... | 178 |
'''simple docstring'''
import qiskit
def lowerCAmelCase_ ( _lowerCamelCase: int = 2 ):
__SCREAMING_SNAKE_CASE : Dict = qubits
# Using Aer's simulator
__SCREAMING_SNAKE_CASE : Optional[Any] = qiskit.Aer.get_backend("""aer_simulator""" )
# Creating a Quantum Circui... | 178 | 1 |
'''simple docstring'''
from __future__ import annotations
from PIL import Image
# Define glider example
UpperCAmelCase_ : Optional[int] = [
[0, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0],
[1, 1, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, ... | 24 |
'''simple docstring'''
from collections import UserDict
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_availabl... | 24 | 1 |
'''simple docstring'''
from math import factorial
def _A ( __magic_name__ = 20 ):
lowercase__ = 2 * n # middle entry of odd rows starting at row 3 is the solution for n = 1,
# 2, 3,...
lowercase__ = n // 2
return int(factorial(lowerCAmelCase__ ) / (factorial(... | 709 |
import argparse
import numpy as np
import torch
from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging
logging.set_verbosity_info()
_snake_case = logging.get_logger("""transformers.models.speecht5""")
def _A ( __magic_name__ , __magic_name__ , __magic_n... | 611 | 0 |
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_tf_available():
import tensorflow as tf
__snake_case : int = logging.get_logger(__name__)
... | 540 |
import sacrebleu as scb
from packaging import version
from sacrebleu import TER
import datasets
SCREAMING_SNAKE_CASE__ : List[Any] = """\
@inproceedings{snover-etal-2006-study,
title = \"A Study of Translation Edit Rate with Targeted Human Annotation\",
author = \"Snover, Matthew ... | 112 | 0 |
"""simple docstring"""
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def UpperCAmelCase ( ):
"""simple docstring"""
A__ = HfArgumentParser(UpperCamelCase__ )
A__ = parser.par... | 536 | """simple docstring"""
import unittest
import numpy as np
from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
load_numpy,
nightly,
require_onnxruntime,
require_torch_gpu,
)
if is_onn... | 536 | 1 |
'''simple docstring'''
import re
from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P
# Sentinels
__lowerCAmelCase =object()
# For specifying empty leaf dict `{}`
__lowerCAmelCase =object()
def a (... | 697 |
'''simple docstring'''
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__lowerCAmelCase ={"configuration_focalnet": ["FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "FocalNetConfig"]}
try:
if not is... | 697 | 1 |
"""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... | 706 |
"""simple docstring"""
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version('>=', '4.25.0')):
raise Op... | 215 | 0 |
"""simple docstring"""
import importlib.metadata
import operator
import re
import sys
from typing import Optional
from packaging import version
lowerCamelCase_ = {
'''<''': operator.lt,
'''<=''': operator.le,
'''==''': operator.eq,
'''!=''': operator.ne,
'''>=''': operator.ge,
... | 95 | import copy
import os
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Dict, Mapping, Optional, Union
if TYPE_CHECKING:
from ...processing_utils import ProcessorMixin
from ...utils import TensorType
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxC... | 197 | 0 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,... | 712 |
'''simple docstring'''
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokeniz... | 11 | 0 |
"""simple docstring"""
import os
import textwrap
import pyarrow as pa
import pytest
from datasets import ClassLabel, Features, Image
from datasets.packaged_modules.csv.csv import Csv
from ..utils import require_pil
@pytest.fixture
def lowerCamelCase ( _UpperCamelCase : str ) -> Optional[An... | 139 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart im... | 139 | 1 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils i... | 539 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DPMSolverMultistepScheduler,
TextToVideoSDPipeline,
UNetaDConditionModel,... | 539 | 1 |
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