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 |
|---|---|---|---|---|
def __a ( lowerCAmelCase_ : int ) -> int:
'''simple docstring'''
if divisor % 5 == 0 or divisor % 2 == 0:
return 0
UpperCAmelCase_= 1
UpperCAmelCase_= 1
while repunit:
UpperCAmelCase_= (10 * repunit + 1) % divisor
re... | 593 |
from dataclasses import asdict, dataclass
from typing import Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A = logging.get_logger(__name__)
# TODO Update this
__A = {
'''facebook/esm-1b''': '''https://huggingface.co/facebook/esm-1b/re... | 593 | 1 |
'''simple docstring'''
import os
import posixpath
import uuid
from dataclasses import dataclass
from typing import TYPE_CHECKING, Iterable, List, Optional, Tuple, Union
import numpy as np
import pyarrow as pa
import datasets
from datasets.arrow_writer import ArrowWriter, ParquetWriter
from datasets.config i... | 720 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Sequence
from typing import Literal
def _lowerCAmelCase ( _lowerCAmelCase , _lowerCAmelCase )-> str | Literal[False]:
__UpperCAmelCase = list(_lowerCAmelCase )
__UpperCAmelCase ... | 617 | 0 |
from collections.abc import Generator
from math import sin
def _A ( _lowercase ) -> bytes:
"""simple docstring"""
if len(_lowercase ) != 32:
raise ValueError('Input must be of length 32' )
__UpperCamelCase = B''
for i in [3, 2, 1, 0]:
... | 1 |
def a__ ( A__ = 5_0_0_0_0_0_0_0 ):
SCREAMING_SNAKE_CASE_ : Union[str, Any] = set()
SCREAMING_SNAKE_CASE_ : Optional[int] = int((limit - 2_4) ** (1 / 2) )
SCREAMING_SNAKE_CASE_ : Dict = set(range(3, prime_square_limit + 1, 2 ) )
... | 101 | 0 |
'''simple docstring'''
import os
def snake_case ( ) -> Optional[int]:
"""simple docstring"""
with open(os.path.dirname(snake_case ) + '/p022_names.txt' ) as file:
lowerCAmelCase = str(file.readlines()[0] )
lowerCAmelCase = names.replace('"' , '' ).... | 514 |
'''simple docstring'''
def snake_case ( snake_case : str ) -> str:
"""simple docstring"""
return " ".join(input_str.split()[::-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 514 | 1 |
# limitations under the License.
from typing import Optional, Tuple, Union
import torch
from diffusers import DiffusionPipeline, ImagePipelineOutput
class a__ ( __snake_case ):
def __init__( self , UpperCAmelCase , UpperCAmelCase ) -> Tuple:
super().__ini... | 559 | from manim import *
class a__ ( __snake_case ):
def __SCREAMING_SNAKE_CASE ( self ) -> Dict:
__a = Rectangle(height=0.5 , width=0.5 )
__a = Rectangle(height=0.46 , width=0.46 ).set_stroke(width=0 )
__a ... | 559 | 1 |
import json
import os
import unittest
from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class __lowercase ( A, unittest.TestCase ):
'''simple docstring'''
_A : i... | 720 | # 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/licenses/LICENSE-2.0
#
# Unless required by a... | 591 | 0 |
'''simple docstring'''
import argparse
from collections import defaultdict
import yaml
__UpperCAmelCase ="docs/source/en/_toctree.yml"
def __lowerCAmelCase ( UpperCamelCase__ ) -> List[str]:
__lowerCamelCase = defaultdict(UpperCamelCase__ )
for doc in... | 546 | '''simple docstring'''
def __lowerCAmelCase ( ) -> Optional[Any]:
__lowerCamelCase = 0
for i in range(1 , 10_01 ):
total += i**i
return str(UpperCamelCase__ )[-10:]
if __name__ == "__main__":
print(solution())
| 546 | 1 |
'''simple docstring'''
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_mode... | 211 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionAttendAndExcitePipeline,
UNetaDConditionModel,
... | 211 | 1 |
"""simple docstring"""
import pprint
import requests
A_ = '''https://zenquotes.io/api'''
def UpperCAmelCase__ ():
"""simple docstring"""
return requests.get(API_ENDPOINT_URL + """/today""" ).json()
def UpperCAmelCase__ ():
"""simple docstring"""
... | 609 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ = logging.get_logger(__name__)
A_ = {
'''s-JoL/Open-Llama-V1''': '''https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json''',
}
cla... | 609 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging
if TYPE_CHECKING:
from ...onnx.config import PatchingSp... | 340 |
'''simple docstring'''
def __a ( lowerCAmelCase__ : list ):
if not grid or not grid[0]:
raise TypeError('''The grid does not contain the appropriate information''' )
for cell_n in range(1 , len(grid[0] ) ):
grid[0][cell_n] += grid[0][cell_n - 1]
a... | 340 | 1 |
from typing import Optional
from urllib.parse import quote
import huggingface_hub as hfh
from packaging import version
def _lowercase ( UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ = None ) -> str:
'''simple docstring'''
if version.parse(hfh.__version... | 472 |
def _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE :list[list[float]] ) -> list[list[float]]:
__lowerCAmelCase : list[list[float]] = []
for data in source_data:
for i, el in enumerate(SCREAMING_SNAKE_CASE ):
if len(SCREAMING_SNAKE_CASE ) < i + 1:
da... | 504 | 0 |
'''simple docstring'''
def _A (lowerCAmelCase__ :int = 2_00 ) -> int:
'''simple docstring'''
_a = [1, 2, 5, 10, 20, 50, 1_00, 2_00]
_a = [0] * (pence + 1)
_a = 1 # base case: 1 way to make 0 pence
for co... | 532 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.... | 532 | 1 |
import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset
from ..utils.generic import ModelOutput
class __SCREAMING_SNAKE_CASE( a_ ):
def __init__( self: List[Any] , UpperCamelCase: int , UpperCamelCase: Dict , ... | 328 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCamelCase : str = logging.get_logger(__name__)
__UpperCamelCase : str = {
"""facebook/timesformer""": """https://huggingface.co/facebook/timesformer/resolve/main/config.jso... | 328 | 1 |
import argparse
from pathlib import Path
from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration
def lowercase ( SCREAMING_SNAKE_CASE__ : List[str] , SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE_... | 705 |
from __future__ import annotations
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import is_tf_available, is_vision_available
from ...test_modeling_tf_common import floats_tensor... | 198 | 0 |
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class lowerCAmelCase__ ( __magic_name__ ):
'''si... | 184 |
import logging
import os
from typing import List, TextIO, Union
from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask
_lowerCamelCase : List[Any] = logging.getLogger(__name__)
class lowerCAmelCase__ ( __magic_name__ ):
''... | 184 | 1 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel... | 121 | """simple docstring"""
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 __a ( _lowercase ... | 121 | 1 |
import random
def A ( lowercase__ : Dict , lowercase__ : str , lowercase__ : Optional[Any] ) -> int:
UpperCamelCase__ :List[Any] = a[left_index]
UpperCamelCase__ :Dict = left_index + 1
for j in range(left_index + 1 , lowercase__ ):
if a[j] < pivot:
UpperCamelC... | 45 |
import numpy as np
import torch
import tqdm
from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler
class lowerCAmelCase_ ( lowercase ):
"""simple docstring"""
de... | 45 | 1 |
import argparse
import json
import math
import os
import time
import traceback
import zipfile
from collections import Counter
import requests
def _A ( _UpperCamelCase , _UpperCamelCase=None ):
_UpperCAmelCase : Optional[int] = None
if token is not None:
_UpperCAmelCase : ... | 416 |
import baseaa
def _A ( _UpperCamelCase ):
return baseaa.baaencode(string.encode('''utf-8''' ) )
def _A ( _UpperCamelCase ):
return baseaa.baadecode(_UpperCamelCase ).decode('''utf-8''' )
if __name__ == "__main__":
UpperCAmelCase__ : Union[str, Any] = 'Hello World!'
... | 416 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase_ : Optional[int] = {
"""configuration_trajectory_transformer""": [
"""TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHI... | 435 | '''simple docstring'''
def __A ( UpperCAmelCase ,UpperCAmelCase ) -> str:
'''simple docstring'''
_UpperCamelCase : str = [0 for i in range(r + 1 )]
# nc0 = 1
_UpperCamelCase : List[Any] = 1
for i in ra... | 435 | 1 |
import argparse
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, Distributed... | 704 |
import math
from numpy import inf
from scipy.integrate import quad
def __lowerCAmelCase ( __lowerCamelCase : float ) -> float:
if num <= 0:
raise ValueError("""math domain error""" )
return quad(__lowerCamelCase , 0 , __lowerCamelCase , args=(__lowerCamelCa... | 456 | 0 |
'''simple docstring'''
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_torch_available():
import torch
if is_torch_tpu... | 603 |
"""simple docstring"""
from typing import Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
... | 650 | 0 |
def SCREAMING_SNAKE_CASE_ ( __lowerCamelCase: int , __lowerCamelCase: int ):
'''simple docstring'''
return number | (1 << position)
def SCREAMING_SNAKE_CASE_ ( __lowerCamelCase: int , __lowerCamelCase: int ):
'''simple docstring'''
return number & ~... | 709 |
from __future__ import annotations
from cmath import sqrt
def SCREAMING_SNAKE_CASE_ ( __lowerCamelCase: int , __lowerCamelCase: int , __lowerCamelCase: int ):
'''simple docstring'''
if a == 0:
raise ValueError("Coefficient 'a' must not be zero." )
lowercase_ = b ... | 601 | 0 |
__a : Any = 8.3_14_45_98
def __magic_name__ ( lowercase_ , lowercase_ ) -> float:
'''simple docstring'''
if temperature < 0:
raise Exception("Temperature cannot be less than 0 K" )
if molar_mass <= 0:
raise Exception("... | 606 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_... | 459 | 0 |
import math
import time
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
import torch_xla.debug.metrics as met
... | 486 |
from math import factorial
UpperCamelCase__ : dict[str, int] = {str(digit): factorial(digit) for digit in range(10)}
def A_( A ):
if not isinstance(A , A ):
raise TypeError("""Parameter number must be int""" )
if number < 0:
raise ValueError("""Parameter nu... | 486 | 1 |
"""simple docstring"""
import webbrowser
from sys import argv
from urllib.parse import parse_qs, quote
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
_lowerCAmelCase = '''%20'''.join(argv[1:]) if len(argv) > 1 else ... | 259 |
import argparse
import glob
import logging
import os
import time
from argparse import Namespace
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from torch.utils.data import DataLoader, TensorDataset
from transformers import glue_compute_metrics as compute_m... | 686 | 0 |
import argparse
import torch
from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging
logging.set_verbosity_info()
def __SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAM... | 701 |
import argparse
import json
import os
import sys
import tempfile
import unittest
from argparse import Namespace
from dataclasses import dataclass, field
from enum import Enum
from pathlib import Path
from typing import List, Literal, Optional
import yaml
from transformers import HfArgumentParser, Trai... | 311 | 0 |
import inspect
import unittest
from transformers import ViTMSNConfig
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 ConfigTester
from .... | 16 |
"""simple docstring"""
def __UpperCamelCase ( SCREAMING_SNAKE_CASE ) -> str:
"""simple docstring"""
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 ... | 163 | 0 |
import argparse
import math
import os
from copy import deepcopy
import torch
from audio_diffusion.models import DiffusionAttnUnetaD
from diffusion import sampling
from torch import nn
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
__lowerCAmelCase = {
'''gwf... | 712 |
import warnings
from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor
__lowerCAmelCase = logging.get_logger(__name__)
class __a ( __UpperCamelCase ):
def __init__( self , *lowerCAmelCase__ , **lowerCAmelCase__... | 335 | 0 |
'''simple docstring'''
import argparse
import random
import joblib
import numpy as np
import torch
from igf.igf import (
SecondaryLearner,
collect_objective_set,
compute_perplexity,
generate_datasets,
load_gpta,
recopy_gpta,
set_seed,
train_secondary_learner,
)
from torch.util... | 94 |
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts... | 513 | 0 |
import requests
def A__ ( lowerCamelCase , lowerCamelCase ) -> None:
UpperCamelCase_: Union[str, Any] = {"""Content-Type""": """application/json"""}
UpperCamelCase_: List[Any] = requests.post(lowerCamelCase , json={"""text""": message_body} ,... | 670 |
from manim import *
class _UpperCamelCase ( _A ):
'''simple docstring'''
def lowerCAmelCase__ ( self : int ):
UpperCamelCase_: Dict = Rectangle(height=0.5 , width=0.5 )
UpperCamelCase_: Dict = Rectangle(height=0.46 ,... | 670 | 1 |
'''simple docstring'''
import argparse
import os
import torch
from transformers import (
XLNetConfig,
XLNetForQuestionAnswering,
XLNetForSequenceClassification,
XLNetLMHeadModel,
load_tf_weights_in_xlnet,
)
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
A ... | 125 |
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
from PIL import Image
from ...utils import (
BaseOutput,
OptionalDependencyNotAvailable,
is_flax_available,
is_k_diffusion_available,
is_k_diffusion_version,
is_o... | 328 | 0 |
'''simple docstring'''
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
... | 47 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import is_tf_available, is_torch_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, SMALL_MODEL_IDENTIFIER, is_pt_tf_cross_test, slow
if is_tf_available():
from transformers import (
... | 47 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
A_ : Dict = {
'configuration_graphormer': ['GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GraphormerConfig'],
}
try:
if... | 57 |
"""simple docstring"""
from itertools import count
def UpperCamelCase__ ( lowercase__ : int = 50 ):
snake_case : List[str] = [1] * min_block_length
for n in count(lowercase__ ):
fill_count_functions.append(1 )
for block_length in range(lowercase__ , ... | 134 | 0 |
'''simple docstring'''
from itertools import product
def UpperCamelCase ( lowercase_ : List[Any] , lowercase_ : int ) -> list[int]:
'''simple docstring'''
lowercase =sides_number
lowercase =max_face_number * dice_number
lowercase =[0] * (max_tot... | 720 |
'''simple docstring'''
from typing import Dict, Iterable, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
IM... | 145 | 0 |
"""simple docstring"""
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 transf... | 673 |
"""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()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyN... | 673 | 1 |
import unittest
from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
_snake... | 54 |
from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def lowerCAmelCase_ ( snake_case_ ):
# A local function to see if a dot lands in the circle.
def is_in_circle(snake_case_,snake_case_ ) -> bool:
_A ... | 54 | 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_ve... | 597 |
'''simple docstring'''
import argparse
import intel_extension_for_pytorch as ipex
import torch
from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline
SCREAMING_SNAKE_CASE_ = argparse.ArgumentParser("Stable Diffusion script with intel optimization", add_help=F... | 597 | 1 |
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 (... | 710 |
import os
from typing import List, Optional, Union
from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
from ...utils import logging
a__ : int = logging.get_logger(__name__)
a__ : Tuple = {"vocab_file": "vocab.txt"}
a__ : int ... | 642 | 0 |
"""simple docstring"""
from collections import defaultdict
from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst
def snake_case_ ( ):
'''simple docstring'''
_lowerCamelCase , _lowerCamelCase : Optional[int] = 9, 14 # noqa: F84... | 83 | import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_albert im... | 197 | 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
_snake_case : List[Any] = logging.get_lo... | 421 |
import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def a_ ( ):
with offline(OfflineSimulationMode.CONNECTION_TIMES_OUT ):
with pytest.raises(lowerCAme... | 421 | 1 |
from __future__ import annotations
def __UpperCamelCase ( lowercase__ : list ) -> list:
'''simple docstring'''
if len(lowercase__ ) == 0:
return []
lowerCAmelCase_ , lowerCAmelCase_ : Optional[int] = min(lowercase__ ), max(lowercase__... | 600 |
from __future__ import annotations
import os
import tempfile
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import is_tensorflow_text_available, is_tf_available
from transformers.testing_utils import require_tensorflow_text, require_tf, slow
from ..test_modeli... | 600 | 1 |
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow
@require_torch
class __UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
def ... | 702 |
'''simple docstring'''
import math_equivalence # From: git+https://github.com/hendrycks/math.git
import datasets
UpperCamelCase_ = "\\n@article{hendrycksmath2021,\n title={Measuring Mathematical Problem Solving With the MATH Dataset},\n author={Dan Hendrycks\n and Collin Burns\n and S... | 599 | 0 |
'''simple docstring'''
import unittest
from transformers import SPIECE_UNDERLINE
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.tokenization_utils import AddedToken
from ...test_t... | 120 |
def A ( _lowerCamelCase ):
'''simple docstring'''
if not isinstance(_lowerCamelCase , _lowerCamelCase ):
_lowerCAmelCase : Union[str, Any] = F"Input value of [number={number}] must be an integer"
raise TypeError(_lowerC... | 500 | 0 |
import argparse
import os.path as osp
import re
import torch
from safetensors.torch import load_file, save_file
# =================#
# UNet Conversion #
# =================#
UpperCAmelCase__ : Union[str, Any] =[
# (stable-diffusion, HF Diffusers)
('''time_embed.0.weight''', '''time_embe... | 269 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
... | 269 | 1 |
'''simple docstring'''
import re
from filelock import FileLock
try:
import nltk
_SCREAMING_SNAKE_CASE : Optional[int] = True
except (ImportError, ModuleNotFoundError):
_SCREAMING_SNAKE_CASE : Optional[Any] = False
if NLTK_AVAILABLE:
with FileLock(".lock") as lock:
... | 400 |
'''simple docstring'''
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
_SCREAMING_SNAKE_CASE : Union[str, Any] = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE : int = ... | 400 | 1 |
'''simple docstring'''
import inspect
import unittest
from transformers import ConvNextConfig
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_backbone_common import Backbone... | 700 |
'''simple docstring'''
import os
from pathlib import Path
from unittest.mock import patch
import pytest
import zstandard as zstd
from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
OfflineModeIsEnabled,
cached_path,
fsspec_get,
fsspec_head,
ftp_g... | 609 | 0 |
"""simple docstring"""
import argparse
import os
import torch
from transformers import (
XLNetConfig,
XLNetForQuestionAnswering,
XLNetForSequenceClassification,
XLNetLMHeadModel,
load_tf_weights_in_xlnet,
)
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, lo... | 82 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCamelCase = logging.get_logger(__name__)
lower... | 82 | 1 |
from typing import Dict, List, Optional, Union
import numpy as np
from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name... | 711 |
import torch
from transformers import AutoModel
class _UpperCamelCase( torch.nn.Module ):
def __init__( self : str , SCREAMING_SNAKE_CASE__ : Tuple="sayef/fsner-bert-base-uncased" ):
'''simple docstring'''
super(SCREAMING_SNAK... | 577 | 0 |
import numpy as np
# Importing the Keras libraries and packages
import tensorflow as tf
from tensorflow.keras import layers, models
if __name__ == "__main__":
# Initialising the CNN
# (Sequential- Building the model layer by layer)
__snake_case = models.Sequential()
# Step 1 - Convo... | 386 |
import unittest
from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
__snake_case = get_tests_dir("fixtures/test_sentencepiece_with_bytefallback.m... | 386 | 1 |
"""simple docstring"""
def _snake_case ( _snake_case : str , _snake_case : str ) -> float:
'''simple docstring'''
def get_matched_characters(_snake_case : str , _snake_case : str ) -> str:
... | 505 |
"""simple docstring"""
import json
import os
from typing import Optional
import numpy as np
from ...feature_extraction_utils import BatchFeature
from ...processing_utils import ProcessorMixin
from ...utils import logging
from ...utils.hub import get_file_from_repo
from ..auto import AutoTokenizer
a =... | 505 | 1 |
"""simple docstring"""
import os
import pytest
from transformers.dynamic_module_utils import get_imports
a ='\nimport os\n'
a ='\ndef foo():\n import os\n return False\n'
a ='\ndef foo():\n def bar():\n if True:\n import os\n return False\n return bar()\n'
a ='\... | 530 | """simple docstring"""
import os
from distutils.util import strtobool
def lowerCamelCase_ ( __lowerCAmelCase , __lowerCAmelCase ) -> Union[str, Any]:
'''simple docstring'''
for e in env_keys:
lowerCamelCase__ =int(os.environ.get(__lowerCAmelCase , ... | 530 | 1 |
def a_ ( lowerCAmelCase_ : Tuple ):
__lowerCAmelCase = []
__lowerCAmelCase = set({'(', '[', '{'} )
__lowerCAmelCase = set({')', ']', '}'} )
__lowerCAmelCase = {'{': '}', '[': ']', '(': ')'}
for i in range(len(__A ) ):
if... | 701 |
_snake_case : List[Any] = '0.18.2'
from .configuration_utils import ConfigMixin
from .utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_inflect_available,
is_invisible_watermark_available,
is_k_diffusion_available,
is_k_diffusion_version,
is_libr... | 421 | 0 |
'''simple docstring'''
from .integrations import (
is_optuna_available,
is_ray_available,
is_sigopt_available,
is_wandb_available,
run_hp_search_optuna,
run_hp_search_ray,
run_hp_search_sigopt,
run_hp_search_wandb,
)
from .trainer_utils import (
HPSearchBackend,
default_hp_sp... | 561 | import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, ByTaTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...test_tokenization_common import TokenizerTesterMixin
if... | 140 | 0 |
# 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 keeping the
# full vocab, merges file, a... | 448 |
__lowerCamelCase : dict[str, float] = {
"km/h": 1.0,
"m/s": 3.6,
"mph": 1.60_93_44,
"knot": 1.8_52,
}
__lowerCamelCase : dict[str, float] = {
"km/h": 1.0,
"m/s": 0.2_77_77_77_78,
"mph": 0.6_21_37_11_92,
"knot": 0.5_39_95_68_03,
}
def A__ ( ... | 448 | 1 |
"""simple docstring"""
class __UpperCAmelCase( __lowercase ):
"""simple docstring"""
pass
class __UpperCAmelCase( __lowercase ):
"""simple docstring"""
pass
class __UpperCAmelCase:
... | 218 |
'''simple docstring'''
from __future__ import annotations
from scipy.special import comb # type: ignore
class UpperCAmelCase_ :
def __init__( self : Union[str, Any] , UpperCAmelCase__ : list[tuple[float, float]] ) -> Optional[int]:
lowerC... | 133 | 0 |
'''simple docstring'''
import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union
import numpy as np
import torch
from .utils import deprecate, is_transformers_available
if is_transformers_available():
import transformers
def __UpperCamelCase ( lo... | 712 |
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... | 326 | 0 |
def A__ (snake_case : int ) -> bool:
return sum(i for i in range(1 , number // 2 + 1 ) if number % i == 0 ) == number
if __name__ == "__main__":
print('''Program to check whether a number is a Perfect number or not...''')
a__ = int(input('''Enter number: ''')... | 279 |
# flake8: noqa
# Lint as: python3
from typing import Dict, List, Optional, Type
from .. import config
from ..utils import logging
from .formatting import (
ArrowFormatter,
CustomFormatter,
Formatter,
PandasFormatter,
PythonFormatter,
TensorFormatter,
format_table,
... | 279 | 1 |
from ..utils import DummyObject, requires_backends
class A_ ( metaclass=__lowerCamelCase ):
'''simple docstring'''
_UpperCamelCase : Optional[int] = ["""keras_nlp"""]
def __init__( self , *snake_case , **snake_case ):
requires_backends(self , ['keras_nlp'] )
| 565 |
from math import factorial
UpperCAmelCase = {str(d): factorial(d) for d in range(10)}
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE ):
return sum(DIGIT_FACTORIAL[d] for d in str(__SCREAMING_SNAKE_CASE ) )
def UpperCAmelCase_ ( ):
lowercase = ... | 565 | 1 |
from __future__ import annotations
def __magic_name__ ( lowerCAmelCase_ , lowerCAmelCase_):
'''simple docstring'''
lowerCamelCase_ : Any = sorted(numsa + numsa)
lowerCamelCase_ ,lowerCamelCase_ : List[str] = divmod(len(lowe... | 250 |
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_torc... | 250 | 1 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicab... | 702 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_A = logging.get_logger(__name__)
_A = {
"bert-base-uncased": "https://huggingface.co/bert-base-uncased/resolve/main/config.js... | 279 | 0 |
'''simple docstring'''
import random
def a_ ( __snake_case : int , __snake_case : Tuple , __snake_case : List[str] ) -> Tuple:
"""simple docstring"""
lowerCamelCase_ =a[left_index]
lowerCamelCase_ =left_index + 1
for j in range(left_... | 676 | def A__ ( lowercase: Any, lowercase: List[Any], lowercase: List[Any]=False ) -> Dict:
if isinstance(lowercase, lowercase ) and isinstance(lowercase, lowercase ):
A : int =len(set_a.intersection(lowercase ) )
if alternati... | 305 | 0 |
"""simple docstring"""
from collections.abc import Sequence
def _A ( _a : Sequence[float] , _a : bool = False ):
"""simple docstring"""
if not arr:
return 0
A = 0 if allow_empty_subarrays else float("""-inf""" ... | 255 |
"""simple docstring"""
import pickle
import unittest
import torch
from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu
@require_cpu
class lowerCamelCase__ ( unittest.TestCase ):
'''simple d... | 255 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ : Union[str, Any] = logging.get_logger(__name__)
UpperCAmelCase_ : List[str] = {
"tanreinama/GPTSAN-2.8B-spout_is_uniform": (
"https://huggingface.co/tanreinama... | 21 |
from pathlib import Path
import fire
from tqdm import tqdm
def lowerCAmelCase_ ( lowerCamelCase="ro" , lowerCamelCase="en" , lowerCamelCase="wmt16" , lowerCamelCase=None ):
try:
import datasets
except (ModuleNotFoundError, ImportError):
raise I... | 21 | 1 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
... | 712 |
import unittest
from transformers import AlbertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, ra... | 688 | 0 |
"""simple docstring"""
import os
from collections import namedtuple
import pytest
from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict
SCREAMING_SNAKE_CASE_ = namedtuple(
'_TestCom... | 34 |
import json
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
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, pre... | 108 | 0 |
"""simple docstring"""
from itertools import count
def _a ( _snake_case = 50 ):
"""simple docstring"""
UpperCAmelCase = [1] * min_block_length
for n in count(a_ ):
fill_count_functions.append(1 )
for block_length in range(a_... | 712 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common imp... | 74 | 0 |
"""simple docstring"""
from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available
if is_datasets_available():
import datasets
... | 247 |
"""simple docstring"""
import warnings
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils... | 247 | 1 |
from __future__ import annotations
def lowerCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__UpperCamelCase , __UpperCamelCase :int = position
__UpperCamelCase :List[str] = [
(y + 1, x + 2),
(y - 1, x + ... | 712 | from __future__ import annotations
import bisect
def lowerCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = 0 , SCREAMING_SNAKE_CASE = -1 ):
'''simple docstring'''
if hi < 0:
__UpperCamelCase :str = len(SCREAMING_SNAK... | 452 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
UpperCamelCase_ = {
"""configuration_biogpt""": ["""BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BioGptConfig"""],
"""toke... | 92 |
'''simple docstring'''
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A = logging.get_logger(__name__)
A = {
'''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json''',
}
class __SCREAMING_SNA... | 125 | 0 |
'''simple docstring'''
def lowerCAmelCase__ ( a_ : str = 2_0_0 ) -> int:
UpperCAmelCase__ : Union[str, Any] = [1, 2, 5, 1_0, 2_0, 5_0, 1_0_0, 2_0_0]
UpperCAmelCase__ : int = [0] * (pence + 1)
UpperCAmelCase__ : Optional[int] ... | 719 |
'''simple docstring'''
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 ... | 599 | 0 |
"""simple docstring"""
import warnings
from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum
from ...processing_utils import ProcessorMixin
if is_torch_available():
import torch
class __lowerCAmelCase ( ... | 391 |
"""simple docstring"""
from typing import List, Optional, Union
import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrateg... | 391 | 1 |
"""simple docstring"""
import math
import time
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
import torch_xla.debug.metrics as met
c... | 370 |
"""simple docstring"""
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE__ = 10, SCREAMING_SNAKE_CASE__ = 1_000, SCREAMING_SNAKE_CASE__ = True ) -> int:
assert (
isinstance(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__ )
and isinstance(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__ ... | 370 | 1 |
"""simple docstring"""
import unittest
from transformers import MobileBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common ... | 19 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
A: Dict = {
"configuration_whisper": ["WHISPER_PRETRAINED_CONFI... | 160 | 0 |
import numpy as np
def lowercase ( SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : Optional[Any] = 1e-1_2 , SCREAMING_SNAKE_CASE__ : Dict = 100 , ) -> Union[str, Any]:
assert np.shape(A... | 710 |
# Note: if you intend to run this script make sure you look under scripts/fsmt/
# to locate the appropriate script to do the work correctly. There is a set of scripts to:
# - download and prepare data and run the conversion script
# - perform eval to get the best hparam into the config
# - generate model_ca... | 198 | 0 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_lowerCamelCase : str = logging.get_logger(__name__)
_lowerCamelCase : Tuple = ... | 403 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer
_snake_case : Tuple = ... | 22 | 0 |
'''simple docstring'''
import multiprocessing
import os
from typing import BinaryIO, Optional, Union
import fsspec
from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..uti... | 710 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__ : Union[str, Any] = logging.get_logger(__name__)
UpperCamelCase__ : Any = {
'sayakpaul/vit-msn-base': 'https://huggingface.co/saya... | 385 | 0 |
import argparse
import pathlib
import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version
from transformers import XLMRobertaConfig, XLMRobertaXLForMaskedLM, XLMRobert... | 67 |
"""simple docstring"""
__lowercase : Union[str, Any] = 8.3_1_4_4_6_2 # Unit - J mol-1 K-1
def lowerCamelCase_ ( _lowerCamelCase : float , _lowerCamelCase : float , _lowerCamelCase : float ):
if moles < 0 or kelvin < 0 or volume < 0:
raise V... | 142 | 0 |
'''simple docstring'''
import unittest
from transformers import DebertaVaTokenizer, DebertaVaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
__UpperCamelCase ... | 270 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__UpperCamelCase : Optional[int] = {
"""configur... | 270 | 1 |
'''simple docstring'''
import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union
import numpy as np
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ) -> np.array:
lowerCamelCase_ = f'''{sampling_rate}'''
lowerCamelCase_ = ... | 42 |
'''simple docstring'''
from math import sqrt
def __UpperCAmelCase ( lowerCamelCase_ = 1_000_000) -> int:
UpperCamelCase__ : int = 0
UpperCamelCase__ : int = 0
UpperCamelCase__ : int
while num_cuboids <= l... | 596 | 0 |
"""simple docstring"""
from math import sqrt
def _snake_case ( UpperCamelCase : Dict ):
UpperCAmelCase : Optional[int] = 0
for i in range(1 , int(sqrt(__UpperCamelCase ) + 1 ) ):
if n % i == 0 and i != sqrt(__UpperCamelCase ):
total += i + n // i
elif i == sqrt(__UpperCamelCase )... | 713 |
"""simple docstring"""
import json
import os
from typing import Optional
import numpy as np
from ...feature_extraction_utils import BatchFeature
from ...processing_utils import ProcessorMixin
from ...utils import logging
from ...utils.hub import get_file_from_repo
from ..auto import AutoTokenizer
A: List[An... | 359 | 0 |
from math import pi, sqrt, tan
def UpperCamelCase ( _A : float )-> float:
"""simple docstring"""
if side_length < 0:
raise ValueError("surface_area_cube() only accepts non-negative values" )
return 6 * side_length**2
def UpperCamelCase ( _A : fl... | 491 |
from dataclasses import dataclass
from typing import Optional
import torch
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .modeling_utils import ModelMixin
@dataclass
class UpperCam... | 491 | 1 |
'''simple docstring'''
import unittest
import numpy as np
def snake_case__ ( _A: np.ndarray , _A: np.ndarray , _A: np.ndarray , _A: np.ndarray | None = None , ) -> np.ndarray:
'''simple docstring'''
lowerCAmelCase = np.sh... | 605 | '''simple docstring'''
from pathlib import Path
import cva
import numpy as np
from matplotlib import pyplot as plt
def snake_case__ ( _A: np.ndarray , _A: np.ndarray , _A: np.ndarray , _A: int , _A: int ) -> np.ndarray:
'''simple do... | 605 | 1 |
"""simple docstring"""
import os
import tempfile
import unittest
from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property
@unittest.skipUnless(os.path.exists(__SCREAMING_S... | 572 |
"""simple docstring"""
import itertools
import os
from collections import Counter, defaultdict
from concurrent.futures import ThreadPoolExecutor, as_completed
import numpy as np
import datasets
from .execute import check_correctness
lowercase_ : str = '''\
@misc{chen2021evaluat... | 572 | 1 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tok... | 254 |
"""simple docstring"""
import timeit
import numpy as np
import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD
def lowerCamelCase ( _snake_case ):
def wrapper(*_snake_case ,**_snake_case ):
UpperCAmelCase__ : str ... | 254 | 1 |
'''simple docstring'''
def UpperCamelCase_ ( A__ , A__ ):
return int((input_a, input_a).count(0 ) == 0 )
def UpperCamelCase_ ( ):
assert and_gate(0 , 0 ) == 0
assert and_gate(0 , 1 ) == 0
assert and_gate(1 , 0 ) == 0
assert and_... | 263 |
from argparse import ArgumentParser, Namespace
from typing import Any, List, Optional
from ..pipelines import Pipeline, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand
try:
from fastapi import Body, FastAPI, HTTPException
from fas... | 458 | 0 |
import json
import os
import unittest
from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property
from .... | 639 |
import random
import unittest
import torch
from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import (
TEXT_GUIDED_IMAGE_INP... | 639 | 1 |
import bza
import gzip
import lzma
import os
import shutil
import struct
import tarfile
import warnings
import zipfile
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Dict, List, Optional, Type, Union
from .. import config
from .filelock import FileLock
from .logging import get_logge... | 344 |
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 (
ProphetNetForConditionalGeneration as Proph... | 344 | 1 |
import numpy as np
def __SCREAMING_SNAKE_CASE ( __UpperCamelCase : Tuple , __UpperCamelCase : List[str] , __UpperCamelCase : Dict , __UpperCamelCase : int , __UpperCamelCase : str ) -> Any:
"""simple docstring"""
SCREAMIN... | 379 | import collections
import os
from typing import List, Optional, Tuple
from transformers.utils import is_jieba_available, requires_backends
if is_jieba_available():
import jieba
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__lowerCamelCase : Any ... | 379 | 1 |
'''simple docstring'''
import importlib
import shutil
import threading
import warnings
from typing import List
import fsspec
import fsspec.asyn
from . import compression
from .hffilesystem import HfFileSystem
lowercase__ : Union[str, Any] = importlib.util.find_s... | 8 |
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
lowerCAmelCase__ = """\
"""
lowerCAmelCase__ = """
Perplexity (PPL) is one of the most common metrics for evaluating lang... | 514 | 0 |
'''simple docstring'''
import numpy as np
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
lowerCamelCase_ = int(np.ceil((x_end - xa) / h ) )
lowerCamelCase_ = np.ze... | 708 |
import argparse
import requests
import torch
from PIL import Image
from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor
def lowerCamelCase_ ( lowerCamelCase__ ):
if "cls_token" in name:
lowerCamelCase_ = name.replace("cls_token" , "vit.em... | 313 | 0 |
import warnings
from ...utils import logging
from .image_processing_poolformer import PoolFormerImageProcessor
__snake_case : List[str] =logging.get_logger(__name__)
class lowerCamelCase__ ( __lowerCamelCase):
'''simple docstring'''
def __init__(self ,*__lowerCamelCas... | 647 |
from itertools import product
def _lowerCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
'''simple docstring'''
A_ = sides_number
A_ = max_face_number * dice_number
A_ = [0] * (max_total + 1)
A_ ... | 203 | 0 |
'''simple docstring'''
import os
from collections import deque
import torch
from torch.utils.data import Dataset
class a ( __magic_name__ ):
def __init__( self : Union[str, Any], SCREAMING_SNAKE_CASE_ : str="", SCREAMING_SNAKE_CASE_ : str="train" ):
asse... | 703 |
'''simple docstring'''
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 (
ProphetNetForCond... | 555 | 0 |
'''simple docstring'''
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Value
from .base import TaskTemplate
@dataclass(frozen=lowercase_ )
class lowerCAmelCase_ ( lowercase_ ):
"""simple docstring"""
... | 582 |
_snake_case = """ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"""
def _A ( __magic_name__ ):
# Make sure the supplied data is a bytes-like object
if not isinstance(__magic_name__ , __magic_name__ ):
lowercase__ = f'''a bytes-like object is re... | 655 | 0 |
from __future__ import annotations
from functools import lru_cache
from math import ceil
snake_case : Dict = 1_0_0
snake_case : Union[str, Any] = set(range(3, NUM_PRIMES, 2))
primes.add(2)
snake_case : int
for prime in range(3, ceil(NUM_PRIMES**0.5), 2):
if prime not in pri... | 182 |
def snake_case__ ( __lowercase ) -> int:
"""simple docstring"""
if divisor % 5 == 0 or divisor % 2 == 0:
return 0
A__ : Tuple = 1
A__ : Union[str, Any] = 1
while repunit:
A__ : Union[str, Any] ... | 182 | 1 |
import argparse
import torch
from torch import nn
from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration
def lowerCAmelCase_ ( __UpperCAmelCase: Optional[Any] ) -> str:
UpperCamelCase__ : Optional[int] = [
'''encoder... | 253 |
import pytest
UpperCAmelCase_ = '__dummy_dataset1__'
UpperCAmelCase_ = '\nimport json\nimport os\n\nimport datasets\n\n\nREPO_URL = "https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/"\nURLS = {"train": REPO_URL + "wikiann-bn-train.jsonl", "validation": REP... | 253 | 1 |
'''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 .tokenizat... | 4 |
'''simple docstring'''
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
BERT_START_DOCSTRING,
Bert... | 4 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
UpperCAmelCase__ ={
"configuration_efficientformer": [
"EFFICIENTFORMER_PRE... | 616 |
"""simple docstring"""
import gc
import random
import unittest
import torch
from diffusers import (
IFImgaImgPipeline,
IFImgaImgSuperResolutionPipeline,
IFInpaintingPipeline,
IFInpaintingSuperResolutionPipeline,
IFPipeline,
IFSuperResolutionPipeline,
)
from diffusers.mod... | 616 | 1 |
'''simple docstring'''
import sys
__SCREAMING_SNAKE_CASE :Any = (
'''73167176531330624919225119674426574742355349194934'''
'''96983520312774506326239578318016984801869478851843'''
'''85861560789112949495459501737958331952853208805511'''
'''125406987471585238630507156932909632952... | 119 |
'''simple docstring'''
import sys
from collections import defaultdict
class A_ :
def __init__( self : Dict ):
_UpperCAmelCase = []
def lowercase ( self : Union[str, Any] , snake_case_ : List[str] ):
return self.node_positi... | 119 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a : Union[str, Any] = logging.get_logger(__name__)
__a : Dict = {
# See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}
class _UpperCamelCase ( _UpperCAmelCase ):
... | 534 | def UpperCAmelCase ( lowercase , lowercase ):
"""simple docstring"""
__lowercase = len(lowercase )
__lowercase = len(lowercase )
__lowercase = [[False for _ in range(m + 1 )] for _ in range(n + 1 )]
__lowercase ... | 534 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowerCAmelCase__ = {
'''configuration_chinese_clip''': [
'''CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''Chine... | 544 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCAmelCase__ = {
'''configuration_blenderbot''': [
... | 544 | 1 |
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