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"""
lowercase__ = [sum(int(c, 10) ** 2 for c in i.__str__()) for i in range(10_0000)]
def _snake_case ( lowercase__ ):
_lowerCamelCase : List[str] = 0
while number:
# Increased Speed Slightly by checking every... | 630 |
"""simple docstring"""
from typing import Any
def _snake_case ( lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ , ):
_validation(
lowercase__ , lowercase__ , lowercase__ ,... | 630 | 1 |
from __future__ import annotations
from math import gcd
def __UpperCamelCase ( _lowerCAmelCase , _lowerCAmelCase = 2 , _lowerCAmelCase = 1 , _lowerCAmelCase = 3 , ) -> int | None:
"""simple docstring"""
if num < 2:
raise ValueError("""The input value cannot b... | 520 |
def __UpperCamelCase ( _lowerCAmelCase , _lowerCAmelCase ) -> Optional[Any]:
"""simple docstring"""
A : Dict = [0 for i in range(r + 1 )]
# nc0 = 1
A : Dict = 1
for i in range(1 , n + 1 ):
# to compute current row from previous row.
... | 520 | 1 |
"""simple docstring"""
import argparse
import glob
import logging
import os
from argparse import Namespace
from importlib import import_module
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from seqeval.metrics import accuracy_sc... | 82 |
'''simple docstring'''
import math
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = 0 , SCREAMING_SNAKE_CASE_ = 0 ) -> list:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = end or len(SCREAMING_SNAKE_CASE_ )
for i in range(SCREAMING_SNAKE_CASE_ ... | 591 | 0 |
"""simple docstring"""
import os
from collections.abc import Iterator
def _lowerCAmelCase ( UpperCAmelCase__ : str = "." ) ->Iterator[str]:
for dir_path, dir_names, filenames in os.walk(UpperCAmelCase__ ):
A__ : Any = [d for d in dir_names if d ... | 498 |
"""simple docstring"""
def _lowerCAmelCase ( UpperCAmelCase__ : int, UpperCAmelCase__ : int ) ->str:
if a < 0 or b < 0:
raise ValueError("""the value of both inputs must be positive""" )
A__ : str = str(bin(UpperCAmelCase__ ... | 498 | 1 |
# 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 app... | 165 |
import os
def _SCREAMING_SNAKE_CASE ( a ) -> int:
__A : Any = len(grid[0] )
__A : Tuple = len(a )
__A : Tuple = 0
__A : Any = 0
__A : Optional[Any] = 0
# ... | 239 | 0 |
'''simple docstring'''
from math import factorial
def _lowerCamelCase ( lowercase : List[Any] = 20 ) -> Dict:
'''simple docstring'''
_a = 2 * n # middle entry of odd rows starting at row 3 is the solution for n = 1,
# 2, 3,...
_a = n... | 717 |
'''simple docstring'''
import os
from datetime import datetime as dt
from github import Github
lowerCAmelCase_ : List[Any] = [
'good first issue',
'feature request',
'wip',
]
def _lowerCamelCase ( ) -> Dict:
_a = Github(os.environ["GITH... | 521 | 0 |
def lowerCAmelCase__( lowercase : int , lowercase : int ) -> List[Any]:
__snake_case : Any = 1 # To kept the Calculated Value
# Since C(n, k) = C(n, n-k)
if k > (n - k):
__snake_case : int = n - k
# Calculate C(n,k)
for i ... | 243 |
"""simple docstring"""
import logging
import re
import pytorch_quantization
import pytorch_quantization.nn as quant_nn
import torch
from pytorch_quantization import calib
from pytorch_quantization.tensor_quant import QuantDescriptor
__lowercase : List[str] = logging.getLogger(__name__)
__l... | 142 | 0 |
import torch
from diffusers import CMStochasticIterativeScheduler
from .test_schedulers import SchedulerCommonTest
class A_ ( _UpperCAmelCase ):
"""simple docstring"""
lowercase : Optional[Any] = (CMStochasticIterativeScheduler,)
lowercase : Any = 10
def lowercase... | 721 |
"""simple docstring"""
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import re... | 509 | 0 |
import re
import string
from collections import Counter
import sacrebleu
import sacremoses
from packaging import version
import datasets
a_ :List[Any] = '\n@inproceedings{xu-etal-2016-optimizing,\n title = {Optimizing Statistical Machine Translation for Text Simplification},\n authors={Xu, Wei an... | 35 |
'''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_tf, re... | 405 | 0 |
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, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import... | 510 |
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 snake_case_ ( a ):
'''... | 510 | 1 |
'''simple docstring'''
import unittest
from .lib import (
Matrix,
Vector,
axpy,
square_zero_matrix,
unit_basis_vector,
zero_vector,
)
class _lowercase ( unittest.TestCase ):
def a ( self : Optional[Any] ) -> None:
__snake_case ... | 56 |
"""simple docstring"""
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
lowercase_ = logging.get... | 695 | 0 |
import argparse
import glob
import importlib.util
import os
import re
import black
from doc_builder.style_doc import style_docstrings_in_code
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
UpperCAmelCase_ : ... | 718 |
# This code is adapted from OpenAI's release
# https://github.com/openai/human-eval/blob/master/human_eval/execution.py
import contextlib
import faulthandler
import io
import multiprocessing
import os
import platform
import signal
import tempfile
def SCREAMING_SNAKE_CASE_ ( __A :... | 443 | 0 |
"""simple docstring"""
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 ImageProcessingS... | 238 |
"""simple docstring"""
import random
import torch
from huggingface_hub import HfApi
from diffusers import UNetaDModel
lowerCamelCase__ : int = HfApi()
lowerCamelCase__ : Tuple = {}
# fmt: off
lowerCamelCase__ : str = torch.tensor([
-0.7515, -1.6883... | 238 | 1 |
'''simple docstring'''
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_Q... | 711 |
'''simple docstring'''
import unittest
from transformers import AutoTokenizer, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
if is_flax_available():
import jax.numpy as jnp
from transformers import FlaxXLMRobe... | 338 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
UpperCamelCase__ : int = {
'''configuration_poolformer''': [
'''POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
... | 578 |
'''simple docstring'''
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class _UpperCamel... | 578 | 1 |
lowerCamelCase__ : int = [0, 2, 4, 6, 8]
lowerCamelCase__ : Dict = [1, 3, 5, 7, 9]
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Any:
if remaining_l... | 721 |
from __future__ import annotations
import pandas as pd
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> list[int]:
snake_case__ = [0] * no_of_processes
snake_case__ = [0] * no_of_processes
... | 208 | 0 |
'''simple docstring'''
from __future__ import annotations
lowerCAmelCase__ = list[list[int]]
# assigning initial values to the grid
lowerCAmelCase__ = [
[3, 0, 6, 5, 0, 8, 4, 0, 0],
[5, 2, 0, 0, 0, 0, 0, 0, 0],
[0, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, ... | 596 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
'facebook/dpr-ctx_encoder-single-nq-base': (
'https://huggingface.co/facebook/dpr... | 596 | 1 |
'''simple docstring'''
import argparse
import os
import re
import tensorflow as tf
import torch
from transformers import BertConfig, BertModel
from transformers.utils import logging
logging.set_verbosity_info()
lowerCAmelCase : Union[str, Any] = logging.get_logger(__name__)
def _A (... | 425 |
'''simple docstring'''
def _A ( A ) -> list:
lowercase : Optional[Any] = False
while is_sorted is False: # Until all the indices are traversed keep looping
lowercase : List[str] = True
for i in range(0 ,len(A ) - 1 ,2 ): # iterating over... | 425 | 1 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging
if TYPE_CHECKING:
fr... | 534 |
import requests
from bsa import BeautifulSoup
def lowerCamelCase__ (__lowerCamelCase = "AAPL" ):
_SCREAMING_SNAKE_CASE : Dict = f"""https://in.finance.yahoo.com/quote/{symbol}?s={symbol}"""
_SCREAMING_SNAKE_CASE : str = BeautifulSoup(reque... | 249 | 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 app... | 629 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
SCREAMING_SNAKE_CASE__ : List[str] = {"""configuration_van""": ["""VAN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """VanConfig"""]}
try:
if not is_torch_av... | 629 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__lowercase : List[Any] =logging.get_logger(__name__)
__lowercase : List[str] ={
"""roberta-base""": "... | 54 |
'''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()
a = [
'wor... | 350 | 0 |
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelerate_available,
is_accelerat... | 597 |
from .testing import (
are_the_same_tensors,
execute_subprocess_async,
require_bnb,
require_cpu,
require_cuda,
require_huggingface_suite,
require_mps,
require_multi_gpu,
require_multi_xpu,
require_safetensors,
require_single_gpu,
require_single_xpu,
require_torch_min... | 597 | 1 |
"""simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConfig,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaForCTC,
WavaVecaForPreTraining,
WavaVecaProcessor,
... | 572 |
"""simple docstring"""
from sklearn.metrics import matthews_corrcoef
import datasets
lowercase_ : Tuple = '''
Compute the Matthews correlation coefficient (MCC)
The Matthews correlation coefficient is used in machine learning as a
measure of the quality of binary and multiclass classif... | 572 | 1 |
"""simple docstring"""
def lowerCamelCase__ ( UpperCAmelCase_ , UpperCAmelCase_ )-> Tuple:
"""simple docstring"""
return (pointa[0] - pointa[0]) ** 2 + (pointa[1] - pointa[1]) ** 2
def lowerCamelCase__ ( UpperCAmelCase_ , UpperCAmelCase_... | 556 |
"""simple docstring"""
import os
from argparse import ArgumentParser
from typing import List
import torch.utils.data
from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node
SCREAMING_SNAKE_CASE = 4
SCREAMING_SNAKE_CASE = 3
class... | 556 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__magic_name__ : int = {
'configuration_ele... | 281 |
"""simple docstring"""
__magic_name__ : Optional[Any] = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/'
def a_ ( lowercase__ :bytes ):
# Make sure the supplied data is a bytes-like object
if not isinstance(lowercase__, lowercase__ ):
... | 281 | 1 |
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conver... | 709 |
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 | 0 |
'''simple docstring'''
UpperCAmelCase_ : Any = '''Alexander Joslin'''
import operator as op
from .stack import Stack
def _UpperCamelCase (_lowerCamelCase : List[Any] )-> int:
'''simple docstring'''
__snake_case = {"""*""": op.mul, """/""": op.t... | 24 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
_A = {"""configuration_unispeech""": ["""UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP""", """UniSpeechCo... | 299 | 0 |
from math import factorial
class snake_case__:
"""simple docstring"""
def __init__( self : List[str] , SCREAMING_SNAKE_CASE : List[Any] , SCREAMING_SNAKE_CASE : Union[str, Any] ):
lowercase__ : Optional[Any] = real
if isins... | 81 |
import inspect
import unittest
from transformers import ConvNextVaConfig
from transformers.models.auto import get_values
from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
... | 81 | 1 |
'''simple docstring'''
import inspect
import unittest
from datasets import load_dataset
from packaging import version
from transformers import BeitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_visi... | 28 |
"""simple docstring"""
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from huggingface_hub import HfFolder, Repository, create_repo, delete_repo
from requests.exceptions import HTTPError
import transformers
from transformers import (
... | 177 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
SCREAMING_SNAKE_CASE_ : Optional[... | 500 |
"""simple docstring"""
import math
import sys
def _snake_case ( UpperCAmelCase_ : int ):
if number != int(UpperCAmelCase_ ):
raise ValueError("""the value of input must be a natural number""" )
if number < 0:
raise ValueError("""the value of in... | 500 | 1 |
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 logging
lowerCamelCase : List[str] = logging.get_logger(__name__)
lowe... | 587 |
import tensorflow as tf
from ...tf_utils import shape_list
class _lowerCamelCase ( tf.keras.layers.Layer ):
"""simple docstring"""
def __init__( self , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase=1... | 243 | 0 |
def _UpperCAmelCase ( a : List[Any] , a : Union[str, Any] ):
snake_case__ = 0
while b > 0:
if b & 1:
res += a
a += a
b >>= 1
return res
def _UpperCAmelCase ( a : str , a : Any , a... | 99 |
import os
import time
import pytest
from datasets.utils.filelock import FileLock, Timeout
def _UpperCAmelCase ( a : List[Any] ):
snake_case__ = FileLock(str(tmpdir / """foo.lock""" ) )
snake_case__ = FileLock(str(tmpdir / """foo.lock""" ) )
snake_case__ = 0.... | 99 | 1 |
"""simple docstring"""
def a__ ( lowerCAmelCase__ ):
UpperCAmelCase_ = 0
UpperCAmelCase_ = len(lowerCAmelCase__ )
for i in range(n - 1 ):
for j in range(i + 1 , lowerCAmelCase__ ):
if arr[i] > arr[j]:
... | 82 |
"""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.... | 93 | 0 |
"""simple docstring"""
def A_ ( __lowercase = 10 , __lowercase = 22 ):
UpperCamelCase_ : Union[str, Any] =range(1 , __lowercase )
UpperCamelCase_ : List[Any] =range(1 , __lowercase )
return sum(
1 for power in powers for base in bases if len(str(base**power )... | 395 |
"""simple docstring"""
import argparse
import ast
import logging
import os
import sys
import pandas as pd
import torch
from tqdm import tqdm
from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as transformers_logg... | 395 | 1 |
'''simple docstring'''
import time
from dataclasses import dataclass
from multiprocessing import Pool
from unittest import TestCase
from unittest.mock import patch
import multiprocess
import numpy as np
import pytest
from datasets.utils.py_utils import (
NestedDataStructure,
asdict,
... | 69 |
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 | 0 |
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,
AdamW,
get_constant_schedule,
get_const... | 284 |
import unittest
from transformers import MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, AutoTokenizer, is_vision_available
from transformers.pipelines import pipeline
from transformers.pipelines.document_question_answering import apply_tesseract
from transformers.testing_utils import (
is_pipeline_test,
... | 284 | 1 |
"""simple docstring"""
import math
import flax.linen as nn
import jax.numpy as jnp
def lowerCamelCase__ ( __snake_case, __snake_case, __snake_case = 1, __snake_case = 1, __snake_case = 1.0e4, __snake_case = False, __snake_case = 1.0, ) -> jnp.ndarray:
"""simple docstr... | 19 |
'''simple docstring'''
class lowerCAmelCase_:
'''simple docstring'''
def __init__( self ,__UpperCAmelCase ) -> Any:
lowerCAmelCase__ : str = n
lowerCAmelCase__ : Optional[Any] = [None] * self.n
lowerCAmelCase__ : Tuple = 0 ... | 565 | 0 |
import argparse
import re
import numpy as np
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SamConfig,
SamImageProcessor,
SamModel,
SamProcessor,
SamVisionConfig,
)
__magic_name__ = {
'''iou_prediction_he... | 719 |
import torch
from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class _SCREAMING_SNAKE_CASE ( __UpperCamelCase ):
_A : Any = (KDPMaDiscreteScheduler,)
_A : Dict = 10
def A_... | 530 | 0 |
"""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
A = logging.get_logger(__name__)
A ... | 52 |
'''simple docstring'''
__lowerCAmelCase ={
"meter": "m",
"kilometer": "km",
"megametre": "Mm",
"gigametre": "Gm",
"terametre": "Tm",
"petametre": "Pm",
"exametre": "Em",
"zettametre": "Zm",
"yottametre": "Ym",
}
# Exponent of the factor(meter)
__lowerCAmelCase ={
"m"... | 697 | 0 |
import json
import os
import unittest
from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers
from ...test_tokenization_common import To... | 59 |
import warnings
from functools import wraps
from typing import Callable
def SCREAMING_SNAKE_CASE__ ( UpperCamelCase__: Callable ):
@wraps(UpperCamelCase__ )
def _inner_fn(*UpperCamelCase__: Dict , **UpperCamelCase__: Any ):
warnings.warn(
(f'... | 59 | 1 |
import argparse
import torch
from transformers import YosoConfig, YosoForMaskedLM
def _UpperCamelCase ( snake_case__ ) -> Union[str, Any]:
if "model" in orig_key:
__UpperCAmelCase : Tuple = orig_key.replace("model.", "" )
if "nor... | 382 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaImgaImgPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, ... | 322 | 0 |
def _A ( _UpperCamelCase ):
if length <= 0 or not isinstance(_UpperCamelCase , _UpperCamelCase ):
raise ValueError('''Length must be a positive integer.''' )
return [n * (2 * n - 1) for n in range(_UpperCamelCase )]
if __name__ == "__main__":
print(hexagonal_numbers(length=5))
... | 416 |
from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors
def _A ( _UpperCamelCase ):
_UpperCAmelCase : Tuple = prime_factors(_UpperCamelCase )
if is_square_free(_UpperCamelCase ):
return -1 if len(_UpperCamelCase ) % 2 else 1
return 0
... | 416 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase : int = logging.get_logger(__name__)
lowerCAmelCase : str = {
"""facebook/xglm-564M""": """https://huggingface.co/facebook/xglm-564M/resolve/main/config.json... | 372 |
'''simple docstring'''
import copy
import inspect
import unittest
from transformers import PretrainedConfig, SwiftFormerConfig
from transformers.testing_utils import (
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_a... | 372 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging
SCREAMING_SNAKE_CASE :... | 229 |
"""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
#
# Un... | 229 | 1 |
'''simple docstring'''
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
a : int = logging.get_logger(__name__)
a : Any = r'''
Arg... | 69 |
'''simple docstring'''
import argparse
from pathlib import Path
import torch
from transformers import OPTConfig, OPTModel
from transformers.utils import logging
logging.set_verbosity_info()
a : List[str] = logging.get_logger(__name__)
def __UpperCAmelCase ( _UpperCAm... | 69 | 1 |
class _lowerCAmelCase :
'''simple docstring'''
def __init__( self : Optional[int] ):
'''simple docstring'''
_snake_case : Any = {}
def UpperCamelCase_ ( self : str ):
'''simple docstring'''
pri... | 710 |
def lowerCamelCase_ ( lowerCAmelCase: int )-> list:
_snake_case : List[Any] = int(lowerCAmelCase )
if n_element < 1:
_snake_case : int = ValueError('a should be a positive number' )
raise my_error
_snake_case : Union[str, Any] ... | 669 | 0 |
import os
import sys
import unittest
lowercase_ = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))
import check_dummies # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init... | 74 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Iterator
from typing import Any
class lowerCAmelCase__ :
def __init__( self : Any , snake_case__ : Any ):
'''simple docstring'''
UpperCAmelCase__ : Any = ... | 438 | 0 |
def lowerCAmelCase__ ( UpperCamelCase_ : List[str] = 4_0_0_0_0_0_0 )-> Dict:
A__ = [0, 1]
A__ = 0
while fib[i] <= n:
fib.append(fib[i] + fib[i + 1] )
if fib[i + 2] > n:
break
i += 1
A__ = 0
for j in range(len(UpperCamelCase... | 714 |
import jax.numpy as jnp
from ...utils import logging
from ..ta.modeling_flax_ta import FlaxTaEncoderModel, FlaxTaForConditionalGeneration, FlaxTaModel
from .configuration_mta import MTaConfig
_lowercase = logging.get_logger(__name__)
_lowercase = "T5Config"
def lowerCAmelCase__ ( Upp... | 526 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available
SCREAMING_SNAKE_CASE_ = {'tokenization_herbert': ['HerbertTokenizer']}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
e... | 582 |
'''simple docstring'''
import argparse
import glob
import importlib.util
import os
import re
import black
from doc_builder.style_doc import style_docstrings_in_code
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
SCREA... | 582 | 1 |
'''simple docstring'''
def UpperCAmelCase_ ( lowercase__ = 5_0 ):
'''simple docstring'''
a_ =[1] * (length + 1)
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_start in ... | 41 |
'''simple docstring'''
import unittest
from typing import Tuple
import torch
from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch
@require_torch
class UpperCAmelCase :
'''simple docstrin... | 41 | 1 |
from __future__ import annotations
from math import pi
from typing import Protocol
import matplotlib.pyplot as plt
import numpy as np
class A ( UpperCAmelCase__ ):
'''simple docstring'''
def lowerCamelCase__ (self : List[str] , _UpperCAmelCase ... | 15 |
"""simple docstring"""
import inspect
import unittest
from huggingface_hub import hf_hub_download
from transformers import ConvNextConfig, UperNetConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import ... | 19 | 0 |
"""simple docstring"""
from __future__ import annotations
def _snake_case ( lowerCamelCase__ : list[int | float] , lowerCamelCase__ : int , lowerCamelCase__ : int ) -> int | float:
if len(lowerCamelCase__ ) == 0:
raise ValueErro... | 711 |
"""simple docstring"""
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class lowercase__ ... | 244 | 0 |
"""simple docstring"""
import os
from typing import BinaryIO, Optional, Union
import numpy as np
import pyarrow.parquet as pq
from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
from ..packaged_module... | 299 |
"""simple docstring"""
import inspect
import unittest
from transformers import YolosConfig
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 Co... | 299 | 1 |
from __future__ import annotations
import math
import numpy as np
from numpy.linalg import norm
def A_ ( A__ , A__ ) -> float:
return math.sqrt(sum(pow(a - b , 2 ) for a, b in zip(A__ , A__ ) ) )
def A_ ( A__ , A__ ) -> list[list[list[float]... | 707 |
# 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 chec... | 392 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowercase_ = {
"""configuration_mvp""": ["""MVP_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MvpConfig""", """MvpOnnxConfig"""],
"""tokenization_mvp""": ["""Mvp... | 413 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
lowerCAmelCase : List[str] = {
"""configuration_gpt_bigcode""": ["""GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTBigCodeConfig"""],
}
try:
i... | 202 | 0 |
import os
import torch
from ..logging import get_logger
from .constants import FSDP_PYTORCH_VERSION, MODEL_NAME, OPTIMIZER_NAME
from .versions import is_torch_version
if is_torch_version(""">=""", FSDP_PYTORCH_VERSION):
import torch.distributed.checkpoint as dist_cp
from torch.distributed.checkpoint.... | 525 | class A_ :
def __init__( self : List[Any] ):
__a = {} # Mapping from char to TrieNode
__a = False
def _UpperCAmelCase ( self : Optional[Any] , __SCREAMING_SNAKE_CASE : list[str] ):
for word in words:
self.insert(__SCREAMING_SNAKE_CA... | 525 | 1 |
"""simple docstring"""
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import numpy as np
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
import tr... | 182 |
"""simple docstring"""
import math
def a__ ( lowerCAmelCase ) -> list[int]:
UpperCAmelCase__ : Optional[int] = []
UpperCAmelCase__ : Union[str, Any] = 2
UpperCAmelCase__ : List[Any] = int(math.sqrt(lowerCAmelCase ) ) # S... | 182 | 1 |
'''simple docstring'''
from argparse import ArgumentParser
from . import BaseTransformersCLICommand
def lowerCAmelCase_ ( __A : Dict ):
'''simple docstring'''
return DownloadCommand(args.model , args.cache_dir , args.force , args.trust_remote_code )
class SCREA... | 692 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase ... | 692 | 1 |
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
def A__ ( SCREAMING_SNAKE_CASE_ : dict ) -> tuple:
... | 32 |
import warnings
from ...utils import logging
from .image_processing_deit import DeiTImageProcessor
_snake_case : List[str] = logging.get_logger(__name__)
class a (_lowerCAmelCase ):
"""simple docstring"""
def __init__( self : List[str] , *lowe... | 81 | 0 |
'''simple docstring'''
import argparse
import json
import os
import pickle
import shutil
import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset
from transformers import (
BertConfig,
BertForMaskedLM,
BertTokenizer,
DistilBertConfig,
DistilBe... | 555 |
'''simple docstring'''
import argparse
import os
import gluonnlp as nlp
import mxnet as mx
import numpy as np
import torch
from gluonnlp.base import get_home_dir
from gluonnlp.model.bert import BERTEncoder
from gluonnlp.model.utils import _load_vocab
from gluonnlp.vocab import Vocab
from packaging import version... | 555 | 1 |
import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple
import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ner import Split,... | 385 |
from __future__ import annotations
from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any
@dataclass
class _lowercase :
_a : int
_a : Node | None = None
_a : Node | None = N... | 385 | 1 |
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
import torch
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils im... | 716 |
_a = "\n# Transformers 설치 방법\n! pip install transformers datasets\n# 마지막 릴리스 대신 소스에서 설치하려면, 위 명령을 주석으로 바꾸고 아래 명령을 해제하세요.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"
_a = [{"type": "code", "content": INSTALL_CONTENT}]
_a = {
"{processor_class}": "FakeProcessorC... | 29 | 0 |
"""simple docstring"""
from typing import List, Optional, Tuple, Union
import torch
from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
_a = logging.get_logger(__name__) # pylint: disable=invalid-name
class ... | 19 |
"""simple docstring"""
import unittest
from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tenso... | 19 | 1 |
from __future__ import annotations
import time
import numpy as np
UpperCAmelCase : Optional[Any] = [8, 5, 9, 7]
UpperCAmelCase : List[str] = [
[2, 0, 1, 1],
[0, 1, 2, 1],
[4, 0, 0, 3],
[0, 2, 1, 0],
[1, 0, 3, 0],
]
UpperCAmelCase : Any = [
[3... | 77 |
def _SCREAMING_SNAKE_CASE ( a ) -> int:
__A : List[str] = []
__A : Tuple = []
__A : Union[str, Any] = {
'^': 3,
'*': 2,
'/': 2,
'%': 2,
'+': 1,
'-': 1,
} # Priori... | 77 | 1 |
'''simple docstring'''
import comet # From: unbabel-comet
import torch
import datasets
snake_case_ = datasets.logging.get_logger(__name__)
snake_case_ = """\
@inproceedings{rei-EtAl:2020:WMT,
author = {Rei, Ricardo and Stewart, Craig and Farinha, Ana C and Lavie, Alon},
title = ... | 507 |
'''simple docstring'''
from math import cos, sin, sqrt, tau
from audio_filters.iir_filter import IIRFilter
def __lowercase (_SCREAMING_SNAKE_CASE :int , _SCREAMING_SNAKE_CASE :int , _SCREAMING_SNAKE_CASE :float = 1 / sqrt(2 ) ):
SCREAMING_SNAKE_CASE : List[str] ... | 507 | 1 |
'''simple docstring'''
import unittest
from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow
if is_flax_available():
import optax
from flax.training.common_utils import onehot
from transformers i... | 47 |
'''simple docstring'''
from __future__ import annotations
def _a ( lowerCAmelCase_ , lowerCAmelCase_ = None , lowerCAmelCase_ = None ):
"""simple docstring"""
if start is None:
_snake_case : Optional[Any] = 0
if end is None:
... | 47 | 1 |
'''simple docstring'''
import argparse
import copy
def _lowerCAmelCase ( __magic_name__ : List[Any] ) -> Tuple:
lowercase : Optional[int] ={}
with open(__magic_name__ ) as f:
for line in f:
if line.split()[0] not in dict_o... | 92 |
import csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter
from transformers import HfArgumentParser
def snake_case( ... | 217 | 0 |
'''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 a__ ( unittest.TestCase ):... | 355 |
'''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.utils.dat... | 355 | 1 |
from argparse import ArgumentParser
from . import BaseTransformersCLICommand
def _a ( lowerCAmelCase )-> Dict:
return DownloadCommand(args.model , args.cache_dir , args.force , args.trust_remote_code )
class lowercase_ (SCREAMING_SNAKE_CASE__ ):
... | 360 |
import torch
from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class lowercase_ (SCREAMING_SNAKE_CASE__ ):
lowerCAmelCase__ =(KDPMaDiscreteScheduler,)
lowerCAmelCase__ =10
... | 360 | 1 |
from json import JSONDecodeError # Workaround for requests.exceptions.JSONDecodeError
import requests
def _lowerCamelCase ( A_ : str = "isbn/0140328726" ) -> dict:
'''simple docstring'''
UpperCamelCase__ : Optional[Any] =olid.strip().strip("/" ) # Remove leading/trailing whitespa... | 582 |
import pyarrow.parquet as pq
import pytest
from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size
from ..utils import assert_arrow_mem... | 582 | 1 |
"""simple docstring"""
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class lowercase__ ( __SCREAMING_SNAKE_CASE ):
"""simple docstring"""
__lowerCAmelCase : Optional[Any] = """Speech2TextFeatur... | 102 |
"""simple docstring"""
import logging
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import arg_to_scheduler
from transformers import TrainingArguments
__magic_name__ : Dict = logging.getLogger(__name__)
@dataclass
c... | 102 | 1 |
"""simple docstring"""
import argparse
import torch
from transformers import (
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaForAudioFrameClassification,
WavaVecaForSequenceClassification,
WavaVecaForXVector,
logging,
)
logging.set_verbosity_info()
_UpperCamelCase : str ... | 710 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
_UpperCamelCase : str = {
"configuration_gpt_neo": ["GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoConfig", "GPTNeoOnnxConfig"],... | 645 | 0 |
from collections import deque
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase ) -> List[Any]:
UpperCamelCase__ : int = len(_A )
UpperCamelCase__ : int = deque()
UpperCamelCase__ : Optional[Any] = [F... | 228 |
from pathlib import Path
from typing import List
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText
if is_torch_avail... | 328 | 0 |
import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel
from ...models.attention import BasicTransformerBlock
from ...utils import logging
__magic_name__ : Dict = logging.get_logger(__name__) # pylint: disable=invalid-name
class lowerCam... | 608 |
from sklearn.metrics import recall_score
import datasets
__magic_name__ : List[str] = '\nRecall is the fraction of the positive examples that were correctly labeled by the model as positive. It can be computed with the equation:\nRecall = TP / (TP + FN)\nWhere TP is the true positives and FN ... | 608 | 1 |
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 import MvpTok... | 280 |
from datetime import datetime as dt
import os
from github import Github
__magic_name__ : Dict = [
'''good first issue''',
'''good second issue''',
'''good difficult issue''',
'''feature request''',
'''new model''',
'''wip''',
]
def lowercase__ ( ... | 280 | 1 |
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():
from ..m... | 242 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase = logging.get_logger(__name__)
_lowercase = {
'google/fnet-base': 'https://huggingface.co/google/fnet-base/resolve/main/config.json',
'google/fnet-large': 'https://huggingface.co/... | 242 | 1 |
from __future__ import annotations
def lowerCAmelCase_ ( __A, __A ) -> list[tuple[int, int]]:
'''simple docstring'''
UpperCAmelCase__ , UpperCAmelCase__ = position
UpperCAmelCase__ = [
(y + 1, x + 2),
(y - 1... | 486 | from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_forma... | 486 | 1 |
'''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
fr... | 700 |
'''simple docstring'''
import argparse
import os
import jax as jnp
import numpy as onp
import torch
import torch.nn as nn
from music_spectrogram_diffusion import inference
from tax import checkpoints
from diffusers import DDPMScheduler, OnnxRuntimeModel, SpectrogramDiffusionPipeline
... | 211 | 0 |
import warnings
from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline # noqa F401
warnings.warn(
"""The `inpainting.py` script is outdated. Please use directly `from diffusers import"""
""" StableDiffusionInpaintPipeline` instead."""
) | 317 |
import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union
from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings
__lowerCamelCase = logging.getLogger(__n... | 317 | 1 |
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
from diffusers.pipelines.kandinsky.text_encoder import... | 702 |
'''simple docstring'''
from ... import PretrainedConfig
lowercase_ : int = {
'''sijunhe/nezha-cn-base''': '''https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json''',
}
class __UpperCamelCase (_UpperCAmelCase ):
__A = NEZHA_PRETRAINED_CONFIG_ARCHIV... | 653 | 0 |
'''simple docstring'''
import argparse
import requests
import torch
from PIL import Image
from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor
def UpperCamelCase_ ( A__ ):
a_ = SwinConfig(image_size=1_92 )
if "base" in model_name:
a_ ... | 263 |
def lowerCAmelCase ( UpperCamelCase__ : List[Any] , UpperCamelCase__ : Dict ) -> List[Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE: int = [0 for i in range(r + 1 )]
# nc0 = 1
__SCREAMING_SNAKE_CASE: Dict = 1
... | 202 | 0 |
from itertools import product
from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros
def a ( SCREAMING_SNAKE_CASE_ : List[Any] , SCREAMING_SNAKE_CASE_ : List[str] ):
"""s... | 643 |
import requests
from bsa import BeautifulSoup
def a ( SCREAMING_SNAKE_CASE_ : str = "AAPL" ):
"""simple docstring"""
UpperCamelCase : Dict = F"""https://in.finance.yahoo.com/quote/{symbol}?s={symbol}"""
UpperCamelCase : Any = ... | 643 | 1 |
'''simple docstring'''
lowercase_ = [0, 2, 4, 6, 8]
lowercase_ = [1, 3, 5, 7, 9]
def lowerCamelCase ( __lowerCamelCase : int , __lowerCamelCase : int , __lowerCamelCase : list[int] , __lowerCamelCase : int ) ->int:
... | 314 |
'''simple docstring'''
from __future__ import annotations
import math
def lowerCamelCase ( __lowerCamelCase : list , __lowerCamelCase : list ) ->list:
if len(__lowerCamelCase ) != 2 or len(a[0] ) != 2 or len(__lowerCamelCase ) != 2 or len(b[0] ) !... | 314 | 1 |
def _lowerCAmelCase ( _a : list ) -> int:
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]
lowerCAmelCase_ : ... | 710 |
from math import acos, sin
from typing import List, Tuple, Union
import numpy as np
import torch
from PIL import Image
from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutp... | 440 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
"facebook/xglm-564M": "https://huggingface.co/facebook/xglm-564M/resolve/main/config.json",
# See all XGLM mod... | 645 |
import json
import os
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
SCREAMING_SNAKE_CASE : List[str] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE : Any = {
"vocab_file": "vocab.json",
... | 419 | 0 |
import os
from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE, hf_cache_home
lowercase : List[str] = HUGGINGFACE_HUB_CACHE
lowercase : Union[str, Any] = 'config.json'
lowercase : List[Any] = 'diffusion_pytorch_model.bin'
lowercase : ... | 704 |
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : int = 200) -> int:
'''simple docstring'''
__UpperCamelCase : Any = [1, 2, 5, 10, 20, 50, 100, 200]
__UpperCamelCase : Any = [0] * (pence + 1)
__UpperCamelCas... | 94 | 0 |
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import MaMaaaTokenizer, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
)
from tr... | 81 |
def lowercase_ ( SCREAMING_SNAKE_CASE : int ):
"""simple docstring"""
if num < 0:
return False
snake_case__ : int =num
snake_case__ : int =0
while num > 0:
snake_case__ : int =rev_num * 10 + (num % 10)
num //= 10
return num_copy ==... | 381 | 0 |
import string
def SCREAMING_SNAKE_CASE ( snake_case_ : str ):
snake_case__ : Union[str, Any] = ""
for i in sequence:
snake_case__ : int = ord(snake_case_ )
if 65 <= extract <= 90:
output += chr(155 - extract )
elif 97 <= extract <= 1... | 25 |
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 ... | 25 | 1 |
from timeit import timeit
def __SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE ) -> int:
if number < 0:
raise ValueError('the value of input must not be negative' )
SCREAMING_SNAKE_CASE_ : Tuple = 0
while number:
number &= number - 1
result += 1
return result
def _... | 345 |
from timeit import timeit
def __SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE ) -> int:
if number < 0:
raise ValueError('the value of input must not be negative' )
SCREAMING_SNAKE_CASE_ : Tuple = 0
while number:
number &= number - 1
result += 1
return result
def _... | 345 | 1 |
"""simple docstring"""
_lowercase : Union[str, Any] = frozenset(
[
"prompt",
"height",
"width",
"guidance_scale",
"negative_prompt",
"prompt_embeds",
"negative_prompt_embeds",
"cross_attention_kwargs",
]
)
_lowercase : T... | 625 |
"""simple docstring"""
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 imp... | 625 | 1 |
"""simple docstring"""
def A_ ( snake_case_ : int ):
'''simple docstring'''
if length <= 0 or not isinstance(snake_case_ ,snake_case_ ):
raise ValueError("""Length must be a positive integer.""" )
return [n * (2 * n - 1) for n in range(snake_case_ ... | 499 |
"""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,
PixaStructProcessor,
... | 499 | 1 |
from __future__ import annotations
def _snake_case ( __snake_case ) -> Optional[int]: # This function is recursive
'''simple docstring'''
UpperCAmelCase_ : Tuple = len(__lowerCAmelCase )
# If the array contains only one element, we return it (... | 718 |
import os
from argparse import ArgumentParser, Namespace
from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand
if not is_tf_available() and ... | 455 | 0 |
'''simple docstring'''
import numpy as np
import torch
from torch.utils.data import DataLoader
from accelerate.utils.dataclasses import DistributedType
class a__ :
'''simple docstring'''
def __init__( self , lowerCamelCase_=... | 90 |
'''simple docstring'''
import re
def _snake_case ( A ) -> bool:
lowerCAmelCase__ = re.compile(R'''^(\+91[\-\s]?)?[0]?(91)?[789]\d{9}$''' )
if match := re.search(A , A ):
return match.string == phone
return False
if _... | 90 | 1 |
"""simple docstring"""
from typing import List, Optional
import numpy as np
from ...processing_utils import ProcessorMixin
from ...utils import to_numpy
class _A ( lowerCAmelCase ):
snake_case__ : Optional[int] = 'EncodecFeatureExtractor'
snake_ca... | 197 | """simple docstring"""
import unittest
from transformers import DebertaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common im... | 197 | 1 |
"""simple docstring"""
import json
import os
import re
import sys
import urllib.request
import requests
from bsa import BeautifulSoup
lowerCamelCase__ : Any = {
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36"
" (KHTML, like Gecko) Chrome/70.0.3538.102 Safa... | 698 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase__ : int = logging.get_logger(__name__)
lowerCamelCase__ : str = {
"studio-ousia/luke-base": "https://huggingface.co/studio-ousia/luke-base/resolve/main... | 698 | 1 |
'''simple docstring'''
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextModel,
CLIPTokenizer,
WhisperForConditionalGeneration,
WhisperProcessor,
)
from diffusers import (
Autoencode... | 449 |
'''simple docstring'''
def UpperCAmelCase ( UpperCAmelCase__ : int = 10_00):
lowerCamelCase : Optional[Any] = 2**power
lowerCamelCase : str = str(UpperCAmelCase__)
lowerCamelCase : Union[str, Any] = list(UpperCAmelCase__)
... | 449 | 1 |
"""simple docstring"""
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Image
from .base import TaskTemplate
@dataclass(frozen=__SCREAMING_SNAKE_CASE )
class __UpperCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple do... | 255 |
"""simple docstring"""
import inspect
import os
import unittest
from dataclasses import dataclass
import torch
from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_... | 695 | 0 |
def snake_case_ ( lowercase__ : list[int] ):
'''simple docstring'''
_lowerCAmelCase =[]
if len(lowercase__ ) == 1:
return [nums.copy()]
for _ in range(len(lowercase__ ) ):
_lowerCAmelCase =nums.pop(0 )
_lowerCAmelCase ... | 149 |
from copy import deepcopy
import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelerate.test_utils import Regr... | 149 | 1 |
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import doctest
import sys
import warnings
from os.path import abspath, dirname, join
import _pytest
from transformers.testing_utils import HfDoctestModule, HfDocTestParser
# allow having multiple repository ch... | 64 | import unittest
import torch
from torch import nn
from diffusers.models.activations import get_activation
class _lowerCamelCase ( unittest.TestCase ):
def UpperCamelCase_ ( self ) -> List[Any]:
SCREAMING_SNAKE_CASE__: Tuple= get_activation('''swish''' )
self.assertIsI... | 64 | 1 |
import hashlib
import unittest
from typing import Dict
import numpy as np
from transformers import (
MODEL_FOR_MASK_GENERATION_MAPPING,
TF_MODEL_FOR_MASK_GENERATION_MAPPING,
is_vision_available,
pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transformers.testing_utils ... | 721 |
import argparse
import logging
import os
import time
import timeit
import datasets
import numpy as np
import pycuda.autoinit # noqa: F401
import pycuda.driver as cuda
import tensorrt as trt
import torch
from absl import logging as absl_logging
from accelerate import Accelerator
from datasets import load_dataset, ... | 447 | 0 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.