code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
"""simple docstring"""
import unittest
from diffusers.pipelines.pipeline_utils import is_safetensors_compatible
class _UpperCAmelCase ( unittest.TestCase):
def __snake_case ( self ) -> List[Any]:
'''simple docstring'''
... | 246 | """simple docstring"""
from .constants import (
MODEL_NAME,
OPTIMIZER_NAME,
RNG_STATE_NAME,
SAFE_WEIGHTS_INDEX_NAME,
SAFE_WEIGHTS_NAME,
SCALER_NAME,
SCHEDULER_NAME,
TORCH_LAUNCH_PARAMS,
WEIGHTS_INDEX_NAME,
WEIGHTS_NAME,
)
from .dataclasses import (
BnbQuantizationConfig,
... | 289 | 0 |
from __future__ import annotations
def lowerCamelCase__ ( a ) -> bool:
_A: List[str] = len(a )
# We need to create solution object to save path.
_A: Optional[Any] = [[0 for _ in range(a )] for _ in range(a )]
_A: List[Any] = run_maze(a , 0 , 0 ... | 301 |
from __future__ import annotations
UpperCAmelCase__ : List[str] = list[list[int]]
# assigning initial values to the grid
UpperCAmelCase__ : Matrix = [
[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, 0, 1, 0, 0, 8, 0]... | 301 | 1 |
'''simple docstring'''
import logging
import os
from typing import List, Tuple
import numpy as np
import psutil
import torch
import torch.distributed as dist
from transformers import RagRetriever
lowerCAmelCase :List[Any] = logging.getLogger(__name__)
class _lowerCamelCase ( lowercase__ ... | 331 |
'''simple docstring'''
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pytest
from datasets.arrow_dataset import Dataset
from datasets.search import ElasticSearchIndex, FaissIndex, MissingIndex
from .utils import requ... | 331 | 1 |
def _lowercase ( UpperCamelCase_ , UpperCamelCase_ ) -> str:
'''simple docstring'''
if a < 0 or b < 0:
raise ValueError('the value of both inputs must be positive' )
SCREAMING_SNAKE_CASE__ = str(bin(UpperCamelCase_ ) )[2:] # remove the leading "... | 350 |
import torch
from diffusers import CMStochasticIterativeScheduler
from .test_schedulers import SchedulerCommonTest
class lowercase__ ( _UpperCAmelCase ):
A__ : Any =(CMStochasticIterativeScheduler,)
A__ : Optional[int] =1_0
def A_ ( self : Dict , ... | 169 | 0 |
'''simple docstring'''
def UpperCamelCase_( snake_case : int ):
'''simple docstring'''
if isinstance(snake_case , snake_case ):
raise TypeError("'float' object cannot be interpreted as an integer" )
if isinstance(snake_case ... | 85 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_SCREAMING_SNAKE_CASE : Tuple = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE : Dict = {
"BridgeTower/bridgetower-ba... | 85 | 1 |
'''simple docstring'''
from typing import Dict, List, Optional, Tuple, 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,
... | 6 |
'''simple docstring'''
def a_ ( __snake_case : int = 1000 ) -> int:
"""simple docstring"""
lowerCamelCase_, lowerCamelCase_ =1, 1
lowerCamelCase_ =2
while True:
lowerCamelCase_ =0
lowerCame... | 6 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
_SCREAMING_SNAKE_CASE : Tuple = {"configuration_gpt_neox": ["GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP",... | 85 |
def SCREAMING_SNAKE_CASE__ ( __a ):
if not isinstance(__a , __a ):
snake_case_ : int = f"""Input value of [number={number}] must be an integer"""
raise TypeError(__a )
if number < 0:
return False
snake_case_ : Dict = number * number
... | 327 | 0 |
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion_sa... | 328 |
from __future__ import annotations
import json
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
_UpperCAmelCase = {'UserAgent': UserAgent().random}
def lowerCAmelCase_ ( UpperCamelCase_ ) -> dict:
UpperCamelCase_ = script.conte... | 328 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCAmelCase : Union[str, Any] = logging.get_logger(__name__)
__lowerCAmelCase : List[str] = {
"google/switch-base-8": "https://huggingface.co/google/switch-base-8/blob/main/config.json",
}
... | 156 |
from typing import Dict
from .base import GenericTensor, Pipeline
class A ( _UpperCAmelCase ):
"""simple docstring"""
def snake_case__ ( self : int,lowercase_ : Dict=None,lowercase_ : Tuple=None,lowercase_ : List[Any]=None,... | 7 | 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
A : Tuple = namedtuple(
"_TestCommandArg... | 259 |
"""simple docstring"""
import string
def _lowerCamelCase ( _UpperCamelCase ):
'''simple docstring'''
__lowerCAmelCase = ""
for i in sequence:
__lowerCAmelCase = ord(_UpperCamelCase )
if 65 <= extract <= 90:
output += chr(155 - extract )
... | 259 | 1 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE = {
"BAAI/AltCLIP": "https://huggingface.co/BAAI/A... | 158 |
'''simple docstring'''
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
@dataclas... | 158 | 1 |
from __future__ import annotations
SCREAMING_SNAKE_CASE__ = [-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0]
SCREAMING_SNAKE_CASE__ = [-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1]
def lowerCAmelCase__ ( _UpperCamelCase : str ) -> List[str]:
... | 355 | """simple docstring"""
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transform... | 149 | 0 |
import ast
import os
import re
import shutil
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.test_utils.examples import compare_against_test
from accelerate.test_utils.testing import TempDirTestCase, require_trackers, run_command, slow
from accelerate.utils impor... | 312 |
import os
from typing import Dict, List, Tuple, TypeVar, Union
__a :Any = TypeVar('T')
__a :Union[str, Any] = Union[List[T], Tuple[T, ...]]
__a :List[str] = Union[T, List[T], Dict[str, T]]
__a :Any = Union[str, bytes, os.PathLike] | 312 | 1 |
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
lowercase : List[Any] = get_tests_dir('''fixtures/test_sentencepi... | 36 |
def lowerCAmelCase__ ( _a : dict ):
snake_case_ : List[Any] = set()
# edges = list of graph's edges
snake_case_ : int = get_edges(_a )
# While there are still elements in edges list, take an arbitrary edge
# (from_node, to_node) and add his extre... | 36 | 1 |
import importlib.metadata
import operator
import re
import sys
from typing import Optional
from packaging import version
lowerCAmelCase__ : int ={
'''<''': operator.lt,
'''<=''': operator.le,
'''==''': operator.eq,
'''!=''': operator.ne,
'''>=''': operator.ge,
'''>''': op... | 257 |
'''simple docstring'''
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 docs... | 323 | 0 |
'''simple docstring'''
from typing import Callable, Optional
from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream
class _UpperCamelCase ( A ):
'''simple docstring'''
def __init__( self : ... | 355 |
'''simple docstring'''
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 _UpperCamelCase ( A ):... | 48 | 0 |
'''simple docstring'''
from __future__ import annotations
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_c... | 163 |
"""simple docstring"""
from __future__ import annotations
def __A ( a_ :str , a_ :str) -> bool:
__a : Optional[Any] = get_failure_array(a_)
# 2) Step through text searching for pattern
__a , __a : Union[str, Any] = ... | 160 | 0 |
'''simple docstring'''
from math import isqrt
def UpperCamelCase_( snake_case : int ):
'''simple docstring'''
return all(number % divisor != 0 for divisor in range(2 , isqrt(snake_case ) + 1 ) )
def UpperCamelCase_( snake_case : i... | 92 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_SCREAMING_SNAKE_CASE : Optional[int] = {
"configuration_canine": ["CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP", "CanineConf... | 92 | 1 |
def lowerCAmelCase__ ( lowerCamelCase_ : int = 1000000):
'''simple docstring'''
lowerCAmelCase__ : Any = [i - 1 for i in range(limit + 1)]
for i in range(2 ,limit + 1):
if phi[i] == i - 1:
for j in range(2 * i ,limit + 1 ,lowerCam... | 129 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__snake_case : Dict =logging.get_logger(__name__)
__snake_case : Optional[int] ={
'facebook/xlm-roberta-xl'... | 129 | 1 |
"""simple docstring"""
from __future__ import annotations
from random import random
from typing import Generic, TypeVar
_UpperCamelCase : int = TypeVar('KT')
_UpperCamelCase : Any = TypeVar('VT')
class snake_case ( Generic[KT, VT] ):
def __init__( se... | 186 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
_UpperCamelCase : Optional[int] = {
'configuration_rag': ['RagConfig'],
'retrieval_rag': ['RagRetriever'],
'tokenization_ra... | 186 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
class snake_case__(__lowerCAmelCase ):
"""simple docstring"""
lowercase_ = '''timm_backbone'''
def __init__( self : Option... | 130 |
"""simple docstring"""
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a = logging.get_logger(__name__)
a = {
'''xlnet-base-cased''': '''https://huggingface.co/xlnet-base-cased/resolve/main/config.json''',
'''xlnet-large-cased''': '''https:/... | 315 | 0 |
"""simple docstring"""
from __future__ import annotations
def _a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> bool:
if len(_SCREAMING_SNAKE_CASE ) == 0:
return False
snake_case_ = len(_SCREAMING_SNAKE_CASE ) // 2
if a_list[midpoint] =... | 233 |
"""simple docstring"""
import shutil
import tempfile
import unittest
from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio
from .test_feature_extraction_clap import floats_list
@requ... | 233 | 1 |
import doctest
from collections import deque
import numpy as np
class lowerCAmelCase__ :
'''simple docstring'''
def __init__( self) -> Union[str, Any]:
_A : List[str] = [2, 1, 2, -1]
_A : List[Any] = [1, 2, 3, 4]... | 11 |
"""simple docstring"""
import inspect
import unittest
import torch
import torch.nn as nn
from accelerate.hooks import (
AlignDevicesHook,
ModelHook,
SequentialHook,
add_hook_to_module,
attach_align_device_hook,
remove_hook_from_module,
remove_hook_from_submodules,
)
from accelerat... | 115 | 0 |
'''simple docstring'''
def UpperCAmelCase_ ( __lowerCamelCase : Tuple ):
lowercase_ :Dict = 0
lowercase_ :Dict = len(__lowerCamelCase )
for i in range(n - 1 ):
for j in range(i + 1 ,__lowerCamelCase ):
if arr[i] > arr... | 147 |
'''simple docstring'''
from typing import List, Optional, Union
import numpy as np
import tensorflow as tf
from .utils import logging
lowerCAmelCase : Tuple =logging.get_logger(__name__)
def UpperCAmelCase_ ( __lowerCamelCase : Union[tf.Tensor, np.ndarray] ):
... | 147 | 1 |
'''simple docstring'''
import argparse
import json
import os
import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile
from transformers.modeling_utils import dtype_byte_size
from transformer... | 163 |
from collections import defaultdict
def __snake_case ( _UpperCAmelCase , _UpperCAmelCase ):
__a = first_str.lower().strip()
__a = second_str.lower().strip()
# Remove whitespace
__a = first_str.replace(''' ''' , '''''' )
__a ... | 49 | 0 |
'''simple docstring'''
import sys
__lowercase : Union[str, Any] = (
'73167176531330624919225119674426574742355349194934'
'96983520312774506326239578318016984801869478851843'
'85861560789112949495459501737958331952853208805511'
'12540698747158523863050715693290963295227443043557'
... | 294 |
'''simple docstring'''
def lowerCamelCase (_SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : int ):
if not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
raise ValueError('iterations must be defined as integers' )
if ... | 294 | 1 |
from __future__ import annotations
import unittest
from transformers import BlenderbotConfig, BlenderbotTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from .... | 5 |
UpperCAmelCase__ = {
'''A''': '''.-''', '''B''': '''-...''', '''C''': '''-.-.''', '''D''': '''-..''', '''E''': '''.''', '''F''': '''..-.''', '''G''': '''--.''',
'''H''': '''....''', '''I''': '''..''', '''J''': '''.---''', '''K''': '''-.-''', '''L''': '''.-..''', '''M''': '''--''', '''N''': '''-.''',... | 5 | 1 |
"""simple docstring"""
import darl # noqa
import gym
import tqdm
from diffusers.experimental import ValueGuidedRLPipeline
UpperCAmelCase = {
'''n_samples''': 64,
'''horizon''': 32,
'''num_inference_steps''': 20,
'''n_guide_steps''': 2, # can set to 0 for faster sampl... | 350 |
"""simple docstring"""
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class __magic_name__ ( __UpperCAme... | 172 | 0 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_i... | 33 |
def A_ ( _UpperCAmelCase , _UpperCAmelCase = False ):
if not isinstance(_UpperCAmelCase , _UpperCAmelCase ):
SCREAMING_SNAKE_CASE_: str = f"Expected string as input, found {type(_UpperCAmelCase )}"
raise ValueError(_UpperCAmelCase )
if not isi... | 13 | 0 |
"""simple docstring"""
_a : Union[str, Any]= "Alexander Joslin"
import operator as op
from .stack import Stack
def __UpperCAmelCase ( UpperCAmelCase_ : str ) -> int:
'''simple docstring'''
__snake_case : Dict = {'*': op.mul, '/': ... | 365 | """simple docstring"""
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
AutoConfig,
AutoImageProcessor,
CLIPConfig,
CLIPImageProcessor,
)
from tra... | 95 | 0 |
'''simple docstring'''
import os
from itertools import chain
from random import randrange, shuffle
import pytest
from .sola import PokerHand
_lowerCamelCase : Optional[int] = (
"4S 3H 2C 7S 5H",
"9D 8H 2C 6S 7H",
"2D 6D 9D TH 7D",
"TC 8C 2S JH 6C",
"JH 8S TH ... | 28 |
'''simple docstring'''
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F
from transformers import DetrImageProcessor, TableTransformerConfi... | 28 | 1 |
'''simple docstring'''
from typing import Optional
import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin... | 331 |
'''simple docstring'''
def a__ ( a__ ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE = len(a__ )
while cur > 1:
# Find the maximum number in arr
__SCREAMING_SNAKE_CASE = arr.index(max(arr[0:cur] ) )
# Reverse from... | 331 | 1 |
"""simple docstring"""
import os
UpperCAmelCase__ : List[str] = {'I': 1, 'V': 5, 'X': 1_0, 'L': 5_0, 'C': 1_0_0, 'D': 5_0_0, 'M': 1_0_0_0}
def lowercase_ ( _snake_case ):
SCREAMING_SNAKE_CASE__ : Tuple = 0
SCREAMING_SNAKE_CASE__ : ... | 25 |
"""simple docstring"""
UpperCAmelCase__ : List[str] = [
9_9_9,
8_0_0,
7_9_9,
6_0_0,
5_9_9,
5_0_0,
4_0_0,
3_9_9,
3_7_7,
3_5_5,
3_3_3,
3_1_1,
2_8_8,
2_6_6,
2_4_4,
2_2_2,
2_0_0,
1_9_9,
1_7_7,
1_... | 25 | 1 |
'''simple docstring'''
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import numpy as np
import pandas as pd
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
BartForSequenceC... | 361 | '''simple docstring'''
import json
import os
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
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 ... | 21 | 0 |
"""simple docstring"""
def lowercase_ ( __UpperCAmelCase ) -> int:
lowerCAmelCase__ : List[str] = []
lowerCAmelCase__ : Union[str, Any] = set({"""(""", """[""", """{"""} )
lowerCAmelCase__ : List[Any] = set({""")""", """]""... | 242 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowerCAmelCase__ = {
'''configuration_pix2struct''': [
'''PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''Pix2Stru... | 108 | 0 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from transformers import XLMRobertaTokenizer
from diffusers import (
AltDiffusionImgaImgPipeline,
AutoencoderKL,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProces... | 24 |
"""simple docstring"""
def lowercase ( _snake_case : int ) ->str:
"""simple docstring"""
if number > 0:
raise ValueError('''input must be a negative integer''' )
__snake_case : Any = len(bin(_snake_case )[3:] )
__snake_case : List[Any] ... | 24 | 1 |
import tempfile
import torch
from diffusers import PNDMScheduler
from .test_schedulers import SchedulerCommonTest
class __UpperCAmelCase ( lowerCamelCase__ ):
UpperCamelCase = (PNDMScheduler,)
UpperCamelCase = (("""num_inference_steps""", 5_0),)
def ... | 336 |
from __future__ import annotations
def a__ ( UpperCAmelCase : list[list[int]] ) -> bool:
UpperCAmelCase : Union[str, Any] = len(UpperCAmelCase )
# We need to create solution object to save path.
UpperCAmelCase : int = [[0 for _ in range(UpperCAmelCase )] fo... | 336 | 1 |
'''simple docstring'''
from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors
def lowercase__ ( __UpperCamelCase )-> int:
UpperCamelCase = prime_factors(__UpperCamelCase )
if is_square_free(__UpperCamelCase ):
... | 183 |
'''simple docstring'''
from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors
def lowercase__ ( __UpperCamelCase )-> int:
UpperCamelCase = prime_factors(__UpperCamelCase )
if is_square_free(__UpperCamelCase ):
... | 183 | 1 |
'''simple docstring'''
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_tf
if is_tf_available():
import tensorflow as tf
from tensorflow.python.eager import context
from tensorflow.python.framework import ops
from transformers import GradientAccumul... | 67 |
def UpperCAmelCase__ ( ):
'''simple docstring'''
return [
a * b * (10_00 - a - b)
for a in range(1 , 9_99 )
for b in range(_A , 9_99 )
if (a * a + b * b == (10_00 - a - b) ** 2)
][0]
if __name__ == "__main__":
print(f"""{solution() = }""... | 188 | 0 |
"""simple docstring"""
import math
import sys
def lowerCamelCase ( _UpperCamelCase : int ) -> int:
'''simple docstring'''
if number != int(_UpperCamelCase ):
raise ValueError("""the value of input must be a natural number""" )
if number < 0... | 365 |
"""simple docstring"""
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image
from tra... | 320 | 0 |
'''simple docstring'''
import os
from bleurt import score # From: git+https://github.com/google-research/bleurt.git
import datasets
lowercase_ = datasets.logging.get_logger(__name__)
lowercase_ = """\
@inproceedings{bleurt,
title={BLEURT: Learning Robust Metrics for Text Generation},
author={T... | 58 |
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union
# Build the list of all feature extractors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...fe... | 26 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__a: List[str] = {
"""configuration_clap""": [
"""CLAP_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""ClapAudioConfig""",
"""ClapConfig""",
... | 214 | '''simple docstring'''
import numpy as np
def __UpperCamelCase ( UpperCAmelCase ):
return 1 / (1 + np.exp(-vector ))
def __UpperCamelCase ( UpperCAmelCase ):
return vector * sigmoid(UpperCAmelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 214 | 1 |
"""simple docstring"""
import copy
import fnmatch
import json
import os
import pickle as pkl
import shutil
import sys
import tarfile
import tempfile
from collections import OrderedDict
from contextlib import contextmanager
from functools import partial
from hashlib import shaaaa
from io import BytesIO
from pathl... | 106 |
"""simple docstring"""
from collections import defaultdict
from math import ceil, sqrt
def __SCREAMING_SNAKE_CASE ( A_ = 1_00_00_00 , A_ = 10 ):
lowerCAmelCase__ : defaultdict = defaultdict(A_ )
for outer_width in range(3 , (t_limit // 4) + 2 ):
if outer_width * outer_wi... | 106 | 1 |
"""simple docstring"""
def UpperCAmelCase ( a_ ):
'''simple docstring'''
if number > 0:
raise ValueError('input must be a negative integer' )
lowerCamelCase : int = len(bin(a_ )[3:] )
lowerCamelCase : int = bin(abs(a_ ... | 205 |
"""simple docstring"""
def UpperCAmelCase ( a_ = 1000 ):
'''simple docstring'''
lowerCamelCase : Dict = 2**power
lowerCamelCase : List[str] = str(a_ )
lowerCamelCase : Dict = list(a_ )
lowerCamelCase : ... | 205 | 1 |
import json
import os
from typing import Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__A =logging.get_logger(__name__)
__A ={'''vocab_file''': '''vocab.json'''}
__A ={
'''vocab_file''': {
'''mgp-str''': '''https://huggingface.co/alibaba-d... | 19 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__A ={
'''configuration_bloom''': ['''BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BloomConfig''', '''BloomOnnxConfig'''],
}
try:
if not is_tokenizers_available():
... | 19 | 1 |
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKIN... | 370 |
"""simple docstring"""
import pytest
from datasets.parallel import ParallelBackendConfig, parallel_backend
from datasets.utils.py_utils import map_nested
from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows
def A_ ( snake_case_ : int ): # picklable for... | 27 | 0 |
"""simple docstring"""
from __future__ import annotations
import numpy as np
def __lowerCamelCase ( __UpperCamelCase ) -> Optional[Any]:
"""simple docstring"""
return np.maximum(0 , __UpperCamelCase )
if __name__ == "__main__":
print(np.array(relu([-1, 0, 5... | 241 |
"""simple docstring"""
import importlib.util
import os
import platform
from argparse import ArgumentParser
import huggingface_hub
from .. import __version__ as version
from ..utils import (
is_accelerate_available,
is_flax_available,
is_safetensors_available,
is_tf_available,
is_torch_avai... | 241 | 1 |
print((lambda quine: quine % quine)("print((lambda quine: quine %% quine)(%r))"))
| 359 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_snake_case = {
"configuration_roc_bert": ["ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "RoCBertConfig"],
"tokenization_roc_bert": ["RoCBertTokenizer"],
}
try:
... | 343 | 0 |
'''simple docstring'''
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__SCREAMING_SNAKE_CASE : str = logging.get_logger(__name__)
__S... | 31 | '''simple docstring'''
import math
import unittest
from transformers import BioGptConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeli... | 31 | 1 |
"""simple docstring"""
from typing import List, Optional, Union
import numpy as np
import PIL.Image
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
PILIm... | 370 |
from __future__ import annotations
from typing import Any
class _SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__(self : Any , UpperCAmelCase_ : int) ->None:
'''simple docstring'''
lowerCamelCase__: int =num_of_nodes
lowerCamelCase__... | 273 | 0 |
"""simple docstring"""
def snake_case__ ( __lowerCamelCase : list ):
"""simple docstring"""
if len(__lowerCamelCase ) < 2:
return collection
def circle_sort_util(__lowerCamelCase : list , __lowerCamelCase : int , __lowerCamelCase : int ) -> bool:
... | 238 |
"""simple docstring"""
import unittest
import numpy as np
import torch
from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device
from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_P... | 238 | 1 |
"""simple docstring"""
from typing import List, Optional, Tuple, Union
import PIL
import torch
from torchvision import transforms
from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor
__A = trans... | 64 | """simple docstring"""
from __future__ import annotations
from collections.abc import Sequence
from typing import Literal
def lowercase_ ( _lowerCamelCase: str , _lowerCamelCase: str ) -> str | Literal[False]:
'''simple docstring'''
__lowerCamelCase : Optional[int] ... | 64 | 1 |
import json
import os
import shutil
import tempfile
import unittest
from transformers import BatchEncoding, CanineTokenizer
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.tokenization_utils import AddedToken
from transformers.utils import cached_property
from .... | 156 |
import numpy as np
from PIL import Image
def UpperCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> np.ndarray:
__lowercase : Optional[int] = np.array(__lowerCAmelCase )
if arr.shape[0] != arr.shape[1]:
raise ValueError('''The input ... | 156 | 1 |
"""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_ = {
'''facebook/data2ve... | 371 |
"""simple docstring"""
import string
# frequency taken from https://en.wikipedia.org/wiki/Letter_frequency
a_ = {
"""E""": 12.70,
"""T""": 9.06,
"""A""": 8.17,
"""O""": 7.51,
"""I""": 6.97,
"""N""": 6.75,
"""S""": 6.33,
"""H""": 6.09,
"""R""": 5.99,
"""D""": 4.25... | 291 | 0 |
'''simple docstring'''
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.models import Sequential
if __name__ == "__main__":
a_ = pd.read_csv('sample_data.csv', header=None)
a_ ... | 152 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device
lowerCamelCase : Optional[int] = False
class A__ ( ... | 47 | 0 |
import gc
import unittest
from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
... | 369 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unles... | 348 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
UpperCAmelCase : Optional[Any] = {
'''configuration_bloom''': ['''BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BloomConfig''', '''BloomOnnxConfig'''],
... | 280 |
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
from torch import nn
... | 280 | 1 |
"""simple docstring"""
from ....utils import logging
_lowercase : List[str] = logging.get_logger(__name__)
class __SCREAMING_SNAKE_CASE ( lowerCAmelCase_ ):
'''simple docstring'''
def __init__( self : List[str], lowerCamelCase : List[Any], lo... | 361 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class __SCREAMING_SNAKE_CASE ( metaclass=lowerCAmelCase_ ):
'''simple docstring'''
_a = ['torch', 'torchsde']
def __init__( self : Union[str, Any], *lowerCamelCase ... | 272 | 0 |
import unittest
from transformers import GPTNeoXJapaneseConfig, is_torch_available
from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import Con... | 127 |
def UpperCAmelCase__ (UpperCamelCase_ ,UpperCamelCase_ ):
"""simple docstring"""
snake_case = [1]
for i in range(2 ,UpperCamelCase_ ):
factorials.append(factorials[-1] * i )
assert 0 <= k < factorials[-1] * n, "k out of bounds"... | 127 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__UpperCAmelCase : int = logging.get_logger(__name__)
__UpperCAmelCase :... | 367 |
'''simple docstring'''
def __A ( lowerCAmelCase_ ):
_UpperCAmelCase : Optional[Any] = 0
while len(lowerCAmelCase_ ) > 1:
_UpperCAmelCase : List[Any] = 0
# Consider two files with minimum cost to be merged
for _ in range(2 ):
_UpperCAmelCase ... | 170 | 0 |
"""simple docstring"""
from __future__ import annotations
def __lowerCAmelCase ( lowercase : List[Any] ) -> bool:
"""simple docstring"""
snake_case : Dict = len(_A )
# We need to create solution object to save path.
snake_case : List[An... | 203 |
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 ..t... | 245 | 0 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
lowerCamelCase :str = logging.get_logger(__na... | 135 |
'''simple docstring'''
import os
import re
import warnings
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
if TYPE_CHECKING:
from ...tokenization_utils_base import TextInput... | 135 | 1 |
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_tokenization_commo... | 184 |
from PIL import Image
def lowercase_ ( _A : Image ):
"""simple docstring"""
lowerCamelCase__ , lowerCamelCase__ : List[str] = image.size
lowerCamelCase__ : Dict = 0
lowerCamelCase__ : Dict = image.load()
for i in range(... | 184 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
__lowerCAmelCase = {
'''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/config.json''',
'''albert-large-v1''': '''h... | 363 |
import json
import os
import shutil
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoConfig, BertConfig, GPTaConfig
from transform... | 288 | 0 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mobilebert import MobileBertTokenizer
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ ... | 5 |
UpperCAmelCase__ = {
'''A''': '''.-''', '''B''': '''-...''', '''C''': '''-.-.''', '''D''': '''-..''', '''E''': '''.''', '''F''': '''..-.''', '''G''': '''--.''',
'''H''': '''....''', '''I''': '''..''', '''J''': '''.---''', '''K''': '''-.-''', '''L''': '''.-..''', '''M''': '''--''', '''N''': '''-.''',... | 5 | 1 |
from packaging import version
from .import_utils import is_accelerate_available
if is_accelerate_available():
import accelerate
def _a ( a :Union[str, Any] ) -> Any:
if not is_accelerate_available():
return method
a = version.parse(accelerate.__version... | 26 |
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 lowercase_ ( lowercase ):
'''simp... | 26 | 1 |
"""simple docstring"""
import os
from datetime import datetime as dt
from github import Github
__A = [
'good first issue',
'feature request',
'wip',
]
def __A () ->int:
"""simple docstring"""
lowerCAmelCase__ :Optional[Any] = Github(os.environ['GI... | 293 |
'''simple docstring'''
import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def _lowerCamel... | 63 | 0 |
import argparse
import logging
import os
import sys
import numpy as np
import onnxruntime
import torch
from bart_onnx.generation_onnx import BARTBeamSearchGenerator
from bart_onnx.reduce_onnx_size import remove_dup_initializers
import transformers
from transformers import BartForConditionalGeneration, BartTokeniz... | 369 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, Features, Value
from .base import TaskTemplate
@dataclass(frozen=lowerCAmelCase )
class _lowercase ( lowerCAmelCase ):
"""simple docstring"""
__A = ... | 71 | 0 |
import os
from tempfile import TemporaryDirectory
from unittest import TestCase
import pytest
from absl.testing import parameterized
from datasets import config
from datasets.arrow_reader import HF_GCP_BASE_URL
from datasets.builder import DatasetBuilder
from datasets.dataset_dict import IterableDatasetDict
from da... | 214 |
import argparse
import shlex
import runhouse as rh
if __name__ == "__main__":
# Refer to https://runhouse-docs.readthedocs-hosted.com/en/latest/api/python/cluster.html#hardware-setup for cloud access
# setup instructions, if using on-demand hardware
# If user passes --user <user> --host <host... | 214 | 1 |
def lowerCAmelCase_ ( __lowerCAmelCase = 50_00_00_00 )-> int:
'''simple docstring'''
UpperCAmelCase : Union[str, Any] =set()
UpperCAmelCase : Optional[int] =int((limit - 24) ** (1 / 2) )
UpperCAmelCase : List[str] =set(range(3... | 78 | from collections import defaultdict
from pathlib import Path
import pandas as pd
from rouge_cli import calculate_rouge_path
from utils import calculate_rouge
__snake_case = [
'''Prosecutor: "No videos were used in the crash investigation" German papers say they saw a cell phone video of the'''
'''... | 78 | 1 |
"""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,
... | 194 |
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_dimen... | 308 | 0 |
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
'huggingface/informer-tourism-monthly': (
'https://huggingface.co/huggingface/informer-tourism-monthly/resol... | 194 |
import datasets
lowercase_ = '\\n@InProceedings{conneau2018xnli,\n author = "Conneau, Alexis\n and Rinott, Ruty\n and Lample, Guillaume\n and Williams, Adina\n and Bowman, Samuel R.\n and Schwenk, Holger\n and S... | 194 | 1 |
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 snake_case ( SCREAMIN... | 317 |
from ..utils import DummyObject, requires_backends
class snake_case ( metaclass=SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
snake_case_ : Optional[Any] = ["""torch"""]
def __init__( self : Union[str, Any] , *lowerC... | 317 | 1 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
SCREAMING_SNAKE_CASE_:Tuple = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_:Optional[Any] = {
"""ut/deta""": """https://huggingface.co/ut/deta/resolve/main/co... | 368 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils impo... | 115 | 0 |
from ..utils import DummyObject, requires_backends
class UpperCamelCase_ ( metaclass=UpperCAmelCase__ ):
'''simple docstring'''
UpperCAmelCase__ = ['''flax''', '''transformers''']
def __init__( self : List[str] , *UpperCAmelCase__ : str , **UpperCA... | 14 |
import argparse
import os
import shutil
import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
def SCREAMING_SNAKE_CASE ( lowercase_ ) -> Dict:
"""simple docstring"""
A__ = args.pruning_method
A__ = ar... | 14 | 1 |
"""simple docstring"""
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from accelerate import Accele... | 56 |
"""simple docstring"""
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> int:
return number | (1 << position)
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> int:
return number & ~(1 << position)
def _lowercase ( __lower... | 56 | 1 |
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def lowerCamelCase__ ( ) -> Optional[int]:
__snake_case = ArgumentParser(
description=(
... | 24 |
import os
import unittest
from transformers.models.cpmant.tokenization_cpmant import VOCAB_FILES_NAMES, CpmAntTokenizer
from transformers.testing_utils import require_jieba, tooslow
from ...test_tokenization_common import TokenizerTesterMixin
@require_jieba
class SCREAMING_SNAKE_CASE__ ( ... | 24 | 1 |
import os
from datetime import datetime as dt
from github import Github
lowerCamelCase_ = [
"good first issue",
"feature request",
"wip",
]
def __lowerCamelCase ( ) -> List[str]:
__SCREAMING_SNAKE_CASE :Optional[Any] = Github... | 369 |
"""simple docstring"""
from typing import Callable, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ = {
"microsoft/xprophetnet-large-wiki100-cased": (
"htt... | 239 | 0 |
'''simple docstring'''
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 import TensorType
class ... | 166 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
__UpperCAmelCase = {
"configuration_layoutlmv2": ["LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP", "LayoutLMv2Config"],
... | 299 | 0 |
"""simple docstring"""
import argparse
import tensorflow as tf
import torch
from transformers import BertConfig, BertForMaskedLM
from transformers.models.bert.modeling_bert import (
BertIntermediate,
BertLayer,
BertOutput,
BertPooler,
BertSelfAttention,
... | 367 |
"""simple docstring"""
import os
def _lowerCAmelCase ( lowerCAmelCase ):
'''simple docstring'''
UpperCAmelCase = len(grid[0] )
UpperCAmelCase = len(lowerCAmelCase )
UpperCAmelCase = 0
UpperCAmelCase ... | 248 | 0 |
"""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
from diffusers.pipelines.sp... | 213 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
A__ : Tuple ={
'''configuration_mvp''': ['''MVP_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MvpConfig''', '''MvpO... | 70 | 0 |
def _a ( SCREAMING_SNAKE_CASE ):
"""simple docstring"""
if not isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
lowercase__ = f'Input value of [number={number}] must be an integer'
raise TypeError(SCREAMING_SNAKE_CASE )
if number < 0:
... | 93 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase = {
'configuration_luke': ['LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LukeConfig'],
'tokenization_luke': ['LukeTokenizer'],
}
try:
if not is_torch_availa... | 93 | 1 |
from typing import Optional, Tuple, Union
import torch
from einops import rearrange, reduce
from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
from diffusers.schedulers.scheduling_ddpm ... | 314 |
'''simple docstring'''
import os
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
_UpperCAmelCase : List[str] = logging.get_logg... | 174 | 0 |
import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor
def __lowerCAmelCase ( __SCREAMING_SNAKE_CASE : dict ):
'... | 20 | from __future__ import annotations
import math
def __lowerCAmelCase ( __SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : bool , __SCREAMING_SNAKE_CASE : list[int] , __SCREAMING_SNAKE_CASE : float ):
... | 20 | 1 |
import json
import os
import unittest
from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property
from ...test_toke... | 328 |
from __future__ import annotations
from collections.abc import Callable
def A_ ( snake_case : Callable[[int | float], int | float] , snake_case : int | float , snake_case : int | float , snake_case : int = 100 , ) ... | 328 | 1 |
from typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, FlaxT... | 257 |
from .data_collator import (
DataCollatorForLanguageModeling,
DataCollatorForPermutationLanguageModeling,
DataCollatorForSeqaSeq,
DataCollatorForSOP,
DataCollatorForTokenClassification,
DataCollatorForWholeWordMask,
DataCollatorWithPadding,
DefaultDataCollator,
default_data_collator... | 257 | 1 |
"""simple docstring"""
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 impor... | 316 |
"""simple docstring"""
import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict
import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info
from transformers import (
AdamW,
AutoConfig,
AutoModel,
AutoModelForPreTraining,
AutoModelF... | 316 | 1 |
"""simple docstring"""
import unittest
from transformers import XLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_model... | 355 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ = {
"google/vivit-b-16x2-kinetics400": (
"https://huggingface.co/google/vivit-b-16x2-kinetics400/resolv... | 239 | 0 |
"""simple docstring"""
from __future__ import annotations
def a__ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
UpperCamelCase = get_failure_array(_SCREAMING_SNAKE_CASE )
# 2) Step through text searching for pattern
UpperCamelCase , ... | 153 |
"""simple docstring"""
def a__ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
if exponent == 1:
return base
if exponent % 2 == 0:
UpperCamelCase = _modexpt(_SCREAMING_SNAKE_CASE , exponent // 2 , _SC... | 153 | 1 |
'''simple docstring'''
import datasets
from .evaluate import evaluate
_lowerCAmelCase = '''\
@article{hendrycks2021cuad,
title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},
author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},
journal={ar... | 184 |
'''simple docstring'''
import unittest
import numpy as np
import requests
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inpu... | 184 | 1 |
"""simple docstring"""
from typing import Any, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler
from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
fr... | 288 | """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 is_t... | 221 | 0 |
import numpy as np
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModelWithProjection, PreTrainedModel
from ...utils import logging
a : str = logging.get_logger(__name__)
class _a ( _lowerCAmelCase ):
A = ... | 360 |
import math
from typing import Optional
import numpy as np
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : Tuple = logging.get_logger(__name__)
a : Optional[Any] = {
'facebook/encodec_24khz': 'https://huggingface.co/facebo... | 82 | 0 |
"""simple docstring"""
def a__ ( snake_case__ , snake_case__ , snake_case__ ) -> list:
lowerCamelCase = len(__A )
lowerCamelCase = [[0] * n for i in range(__A )]
for i in range(__A ):
lowerCamelCase = y_points[i]
for... | 291 |
'''simple docstring'''
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
lowercase : Dict = 200
# Number of elements selected in every generation of evolution. The selection takes
# place from best to wors... | 42 | 0 |
"""simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_a = logging.get_logger(__name__)
_a = {
'asapp/sew-d-tiny-100k': 'https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json',
# See all ... | 360 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_a = logging.get_logger(__name__)
_a = {'ctrl': 'https://huggingface.co/ctrl/resolve/main/config.json'}
class A_ (lowercase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__... | 23 | 0 |
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 impo... | 281 |
import argparse
import collections
import json
import os
import re
import string
import sys
import numpy as np
snake_case : Dict = re.compile(R"\b(a|an|the)\b", re.UNICODE)
snake_case : Optional[int] = None
def lowerCAmelCase_ ( ) -> Union[str, Any]:
'''simp... | 281 | 1 |
"""simple docstring"""
def _a ( _SCREAMING_SNAKE_CASE = 1_000_000 ) -> int:
snake_case_ = 1
snake_case_ = 1
snake_case_ = {1: 1}
for inputa in range(2 , _SCREAMING_SNAKE_CASE ):
snake_case_ = 0
... | 233 |
"""simple docstring"""
import argparse
import json
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
fro... | 233 | 1 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_availab... | 307 |
class __SCREAMING_SNAKE_CASE( a_ ):
pass
class __SCREAMING_SNAKE_CASE( a_ ):
pass
class __SCREAMING_SNAKE_CASE:
def __init__( self: List[str] ) -> Union[str, Any]:
snake_case__ = [
[],
... | 307 | 1 |
import shutil
import tempfile
import unittest
from unittest.mock import patch
from transformers import (
DefaultFlowCallback,
IntervalStrategy,
PrinterCallback,
ProgressCallback,
Trainer,
TrainerCallback,
TrainingArguments,
is_torch_available,
)
from transformers.testing_utils imp... | 371 |
import shutil
import tempfile
import unittest
from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio
from .test_feature_extraction_clap import floats_list
@require_torchaudio
@require... | 70 | 0 |
"""simple docstring"""
import itertools
from dataclasses import dataclass
from typing import Optional
import pandas as pd
import pyarrow as pa
import datasets
from datasets.table import table_cast
@dataclass
class SCREAMING_SNAKE_CASE__ ( datasets.BuilderConfig ):
"""simple docstring""... | 108 |
"""simple docstring"""
import random
import unittest
from torch.utils.data import BatchSampler, DataLoader, IterableDataset
from accelerate import Accelerator
from accelerate.data_loader import (
BatchSamplerShard,
DataLoaderDispatcher,
DataLoaderShard,
IterableDatasetShard,
SkipBatchSam... | 108 | 1 |
'''simple docstring'''
import argparse
import shutil
from pathlib import Path
from tqdm import tqdm
from transformers import AutoTokenizer
def __UpperCamelCase ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase=1024 ):
lowercase__ , lowercase__ : List[str... | 214 | '''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device
from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UN... | 214 | 1 |
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