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'''
import os
import sys
lowerCamelCase : Dict = os.path.join(os.path.dirname(__file__), "src")
sys.path.append(SRC_DIR)
from transformers import (
AutoConfig,
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForQuestionAnswering,
AutoModelForSeq... | 405 | def lowerCamelCase_ ( UpperCamelCase__ : int, UpperCamelCase__ : int ):
'''simple docstring'''
if b == 0:
return 1
if (b % 2) == 0:
return actual_power(UpperCamelCase__, int(b / 2 ) ) * actual_power(UpperCamelCase_... | 240 | 0 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from torch import nn
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import KandinskyV... | 417 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
__UpperCamelCase : Optional[Any] = {
'''configuration_convnext''': ['''CONVNEXT... | 417 | 1 |
'''simple docstring'''
import numpy as np
from PIL import Image
def A__ ( A_ , A_ , A_ ) -> Optional[int]:
_lowercase = np.array(lowerCamelCase_ )
if arr.shape[0] != arr.shape[1]:
raise ValueError("The input array is not a square matrix" )
_lowerca... | 497 |
'''simple docstring'''
from __future__ import annotations
import pandas as pd
def UpperCAmelCase_ ( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ):
"""simple docstring"""
lowerCAmelCase__ : Tuple = [0] * no_of_processes
lowerCAmelCase__ : Any = [0]... | 378 | 0 |
'''simple docstring'''
import os
import posixpath
import uuid
from dataclasses import dataclass
from typing import TYPE_CHECKING, Iterable, List, Optional, Tuple, Union
import numpy as np
import pyarrow as pa
import datasets
from datasets.arrow_writer import ArrowWriter, ParquetWriter
from datas... | 204 | '''simple docstring'''
import argparse
import logging
import pickle
from collections import Counter
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", level=logging.INFO
)
lowerCAmelCase_ : Optional[int] = logging.get... | 204 | 1 |
"""simple docstring"""
import unittest
import numpy as np
import timeout_decorator # noqa
from transformers import BlenderbotSmallConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
... | 52 |
'''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_ = logging.get_logger(__name__)... | 330 | 0 |
from __future__ import annotations
import inspect
import unittest
from math import floor
import numpy as np
from transformers import CvtConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...t... | 649 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCamelCase : int = {
'''configuration_funnel''': ['''FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Funnel... | 649 | 1 |
'''simple docstring'''
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ ) -> list:
'''simple docstring'''
snake_case : Tuple = [0] * len(lowerCamelCase__ )
for i in range(1 , len(lowerCamelCase__ ) ):
# use last results for better performance ... | 638 |
from pathlib import Path
import cva
import numpy as np
from matplotlib import pyplot as plt
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) -> np.ndarray:
__lowerCamelCas... | 652 | 0 |
'''simple docstring'''
def __A ( lowerCamelCase_ ):
"""simple docstring"""
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]
SCREAMING_SNAKE_... | 704 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_flava import FlavaImageProcessor
__UpperCAmelCase = logging.get_logger(__name__)
class UpperCamelCase__ ( lowercase_ ):
"""simple docstring"""
def __init__( self : Dict... | 79 | 0 |
"""simple docstring"""
from __future__ import annotations
from collections import namedtuple
def lowerCAmelCase_ ( lowercase_ : float , lowercase_ : float , lowercase_ : float ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : List[Any] = n... | 674 |
"""simple docstring"""
from collections.abc import Callable
import numpy as np
def lowerCAmelCase_ ( lowercase_ : Callable , lowercase_ : float , lowercase_ : float , lowercase_ : float , lowercase_ : float ):
'''simple docstring'''
__SCREA... | 674 | 1 |
"""simple docstring"""
def lowerCAmelCase__ ( lowerCamelCase__ ) -> list[int]:
if num <= 0:
raise ValueError('Input must be a positive integer' )
A = [True] * (num + 1)
A = 2
while p * p <= num:
if primes[p]:... | 714 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A = logging.get_logger(__name__)
A = {
'edbeeching/decision-transformer-gym-hopper-medium': (
'https://huggingface.co/edbeeching/decision-transformer-gym-hopper-medium/resolve/main... | 109 | 0 |
from __future__ import annotations
from fractions import Fraction
from math import gcd, sqrt
def SCREAMING_SNAKE_CASE_ ( UpperCamelCase__ ):
UpperCamelCase__ : int = int(number**0.5 )
return number == sq * sq
def SCREAMING_SNAKE_CASE_ ( UpperCamelCase__ , U... | 285 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, BlipaProcessor, BlipImageProcessor... | 285 | 1 |
"""simple docstring"""
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class UpperCamelCase_ (__A ):
__magic_name__ = ['''image_processor''', '''tokenizer''']
__magic_name__ = '''AutoImageProcessor'''
__magic_name__ = ... | 463 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ ... | 463 | 1 |
'''simple docstring'''
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except ... | 275 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCAmelCase = logging.get_logger(__name__)
_lowerCAmelCase = {
"""microsoft/swinv2-tiny-patch4-window8-256""": (
"""https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256/resolve/m... | 137 | 0 |
"""simple docstring"""
def lowerCAmelCase_ ( UpperCamelCase__ : int , UpperCamelCase__ : list ):
"""simple docstring"""
_enforce_args(UpperCamelCase__ , UpperCamelCase__ )
if n == 0:
return 0
__lowercase = float("""-inf""" )
for i in range(1 ,... | 442 |
"""simple docstring"""
import os
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from datasets import Dataset
from transformers.models.realm.configuration_realm import RealmConfig
from transformers.models.realm.retrieval_realm import _R... | 442 | 1 |
import copy
from typing import Dict, List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
a__ : Optional[int] = {
"""facebook/mask2former-swin-small-coco-instance""": (
"""https://huggingface.co/facebook... | 165 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a__ : List[str] = logging.get_logger(__name__)
a__ : Tuple = {
"""facebook/s2t-wav2vec2-large-en-de""": (
"""https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/c... | 165 | 1 |
'''simple docstring'''
import unittest
from dataclasses import dataclass
import pytest
from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict
@dataclass
class lowerCAmelCase__ (... | 418 |
'''simple docstring'''
import os
import re
import shutil
import sys
import tempfile
import unittest
import black
__lowerCamelCase : Optional[int] = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))
im... | 418 | 1 |
"""simple docstring"""
# Imports
import numpy as np
class _UpperCAmelCase :
def __init__( self : Tuple , _lowercase : int=None , _lowercase : List[str]=None , _lowercase : Optional[Any]=None , _lowercase : List[Any]=Non... | 49 |
"""simple docstring"""
def lowercase__ ( snake_case_ :float , snake_case_ :float ):
if density <= 0:
raise ValueError('''Impossible fluid density''' )
if bulk_modulus <= 0:
raise ValueError('''Impossible bulk modulus''' )
return (bulk_modulus / density) ** 0... | 49 | 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
lowercase__ : List[str] = logging.get_lo... | 317 |
"""simple docstring"""
def UpperCamelCase_ ( lowerCAmelCase__ : list[int] , lowerCAmelCase__ : list[int] , lowerCAmelCase__ : int ) -> bool:
"""simple docstring"""
return not any(
neighbour == 1 and colored_vertic... | 317 | 1 |
from argparse import ArgumentParser
from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand
UpperCAmelCase_ = logging.get_logger(__name__) # pylint: disable=invalid-name
def __mag... | 458 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.u... | 638 | 0 |
"""simple docstring"""
def A__ ( A__ ) -> bool:
'''simple docstring'''
_UpperCAmelCase = (1 + 24 * n) ** 0.5
return ((1 + root) / 6) % 1 == 0
def A__ ( A__ = 5000 ) -> int:
'''simple docstring'''
_UpperCAmelCase = [(i * (3 * i - 1... | 579 |
"""simple docstring"""
from timeit import timeit
def A__ ( A__ ) -> int:
'''simple docstring'''
if number < 0:
raise ValueError("the value of input must not be negative" )
_UpperCAmelCase = 0
while number:
number &= number - 1
result += 1
ret... | 579 | 1 |
import re
from filelock import FileLock
try:
import nltk
a__ = True
except (ImportError, ModuleNotFoundError):
a__ = False
if NLTK_AVAILABLE:
with FileLock(""".lock""") as lock:
nltk.download("""punkt""", quiet=True)
def lowercase ( SCREAMING_SNAK... | 477 |
"""simple docstring"""
import argparse
import importlib
from pathlib import Path
# Test all the extensions added in the setup
SCREAMING_SNAKE_CASE_ = [
'''kernels/rwkv/wkv_cuda.cu''',
'''kernels/rwkv/wkv_op.cpp''',
'''kernels/deformable_detr/ms_deform_attn.h''',
'''kernels/deform... | 465 | 0 |
'''simple docstring'''
class A :
def __init__( self , SCREAMING_SNAKE_CASE = "" , SCREAMING_SNAKE_CASE = False ) -> None:
"""simple docstring"""
A : dict[str, RadixNode] = {}
# A node will be ... | 343 |
'''simple docstring'''
import numpy as np
def lowerCAmelCase_ ( snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ ):
'''simple docstring'''
A : Optional[Any] = int(np.ceil((x_end - xa) / h ) )
A... | 343 | 1 |
'''simple docstring'''
import argparse
from typing import List
import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset
# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
from torch.optim im... | 22 |
'''simple docstring'''
import torch
from diffusers import DDPMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class A ( _a ):
lowercase_ = (DDPMParallelScheduler,)
def __lowerCAmelCase ( self : ... | 22 | 1 |
import argparse
import os
import torch
from transformers import FlavaImageCodebook, FlavaImageCodebookConfig
def __UpperCamelCase ( a, a, a, a) ->int:
lowerCamelCase__ = s.rsplit(a, a)
return new.join(a)
def __UpperCamelCase ( a) ->int:
... | 360 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging
if TYPE_CHECKING:
from ...onnx.config import PatchingSpec
from ...tok... | 360 | 1 |
import math
from typing import Callable, List, Optional, Union
import numpy as np
import PIL
import torch
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion.pipelin... | 67 |
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... | 205 | 0 |
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class A__ ( a__ ):
'''simple docstring'''
@staticmethod
@abstractmethod
def _SCREAMING_SNAKE_CASE ( _SCREAMING_SNAKE_CASE : int ):
... | 710 |
from __future__ import annotations
import math
import random
from typing import Any
class A__ :
'''simple docstring'''
def __init__( self : str ):
"""simple docstring"""
UpperCamelCase = []
... | 410 | 0 |
"""simple docstring"""
import logging
import random
import ray
from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex
_A = logging.getLogger(__name__)
class __UpperCAmelCase :
"""simple docstring"""
def ... | 505 |
"""simple docstring"""
import argparse
import os
import shutil
from pathlib import Path
import onnx
import torch
from packaging import version
from torch.onnx import export
from diffusers import OnnxRuntimeModel, OnnxStableDiffusionPipeline, StableDiffusionPipeline
UpperCamelCase : List[Any] = ... | 690 | 0 |
import unittest
import numpy as np
import torch
from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class A ( unittest.TestCase ):
'''simp... | 206 |
import timeit
import numpy as np
import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD
def __lowerCAmelCase ( __magic_name__ ):
def wrapper(*__magic_name__ , **__magic_name__ ):
_lowercase: Union[str, Any] = timeit.defa... | 206 | 1 |
from dataclasses import dataclass
from typing import Dict, Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .attention_processor import AttentionProcessor, AttnProce... | 57 | def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: str , lowerCAmelCase_: str ):
def get_matched_characters(lowerCAmelCase_: str , lowerCAmelCase_: str ) -> str:
snake_case_ : Tuple = []
snake_case_ : Tuple = min(len(_stra ) ... | 666 | 0 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase_ : int = {"configuration_mmbt": ["MMBTConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalD... | 702 |
UpperCAmelCase_ : List[str] = [4, 1, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
UpperCAmelCase_ : Any = [3, 7, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
UpperCAmelCase_ : Dict = {
0: "Sunday",
1: "Monday",
2: "Tuesday",
3: "Wednesday",
4: "Thursday",
... | 232 | 0 |
"""simple docstring"""
def lowercase__ ( snake_case_ :list[int] , snake_case_ :str ):
__UpperCAmelCase = int(snake_case_ )
# Initialize Result
__UpperCAmelCase = []
# Traverse through all denomination
for denomination in reversed(snake_case_ ):
... | 49 |
"""simple docstring"""
from collections import deque
class _UpperCAmelCase :
def __init__( self : List[Any] , _lowercase : str , _lowercase : int , _lowercase : int ):
__UpperCAmelCase = process_name # process name
_... | 49 | 1 |
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
_a: Optional[Any] = abspath(join(dirname(dirname(__file__)), """src"""))
s... | 719 |
from __future__ import annotations
import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module
import numpy as np
from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_available, is_vision_available
from... | 268 | 0 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
"Salesforce/blip-vqa-base": "https://huggingface.co/Salesforce/blip-vqa-base/resolve/main/config.json",
... | 59 |
from __future__ import annotations
from decimal import Decimal
from math import * # noqa: F403
from sympy import diff
def lowerCAmelCase_ ( __a , __a , __a = 10**-10 ) -> float:
"""simple docstring"""
lowerCamelCase__: List[str] =a
while Tru... | 59 | 1 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_av... | 433 |
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
__snake_case : Optional[Any] = '\\n\n'
__snake_case : List[Any] = '\nPerplexity (PPL) is one of t... | 433 | 1 |
"""simple docstring"""
from math import sqrt
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE__ ) -> int:
a_ : Dict = 0
for i in range(1, int(sqrt(SCREAMING_SNAKE_CASE__ ) + 1 ) ):
if n % i == 0 and i != sqrt(SCREAMING_SNAKE_CASE__ ):
total += i + ... | 237 |
"""simple docstring"""
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class snake_case_ ( a_ ):
__lowerCA... | 237 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
a_ = {
'configuration_gpt_bigcode': ['GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTBigCodeConfig'],
}
try:
if not is_torch_avail... | 707 |
"""simple docstring"""
import json
import os
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from requests.exceptions import HTTPError
from transformers.utils import (
CONFIG_NAME,
FLAX_WEIGHTS_NAME,
TF2_WEIGHTS_NAME,
TRANSFORMERS_C... | 523 | 0 |
'''simple docstring'''
from typing import Any
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , ) -> list:
_validation(
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , ... | 75 |
import numpy as np
_A = [
["""a""", """b""", """c""", """d""", """e"""],
["""f""", """g""", """h""", """i""", """k"""],
["""l""", """m""", """n""", """o""", """p"""],
["""q""", """r""", """s""", """t""", """u"""],
["""v""", """w""", """x""", """y""", """z"""],
]
... | 258 | 0 |
import os
import sys
import transformers
lowerCAmelCase_: Tuple = "3"
print("Python version:", sys.version)
print("transformers version:", transformers.__version__)
try:
import torch
print("Torch version:", torch.__version__)
print("Cuda available:", torch.cuda.is_available())
print("Cuda version:... | 700 | """simple docstring"""
import unittest
from huggingface_hub import hf_hub_download
from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VideoMAEFeatureExtractor
from transformers.pipelines import VideoClassificationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nes... | 668 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ : Optional[Any] = logging.get_logger(__name__)
UpperCAmelCase_ : Union[str, Any] = {
'microsoft/markuplm-base': 'https://huggingface.co/microsoft/markuplm-base/re... | 44 |
'''simple docstring'''
import tempfile
import unittest
import numpy as np
import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow
from ...generation.test_f... | 442 | 0 |
"""simple docstring"""
from typing import Optional
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_star... | 137 |
"""simple docstring"""
from math import asin, atan, cos, radians, sin, sqrt, tan
_SCREAMING_SNAKE_CASE : Dict = 637_8137.0
_SCREAMING_SNAKE_CASE : Any = 635_6752.31_4245
_SCREAMING_SNAKE_CASE : List[Any] = 637_8137
... | 137 | 1 |
'''simple docstring'''
from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import pyarrow as pa
if TYPE_CHECKING:
from .features import FeatureType
@dataclass
class __snake_case :
'''simple docstring'''
lowerCame... | 38 |
'''simple docstring'''
import argparse
import json
import math
import os
import time
import traceback
import zipfile
from collections import Counter
import requests
def UpperCamelCase__ ( __magic_name__ : str , __magic_name__ : List[Any]=None ) -> Union[str, Any]:
''... | 38 | 1 |
'''simple docstring'''
import itertools
import random
import unittest
import numpy as np
from transformers import ASTFeatureExtractor
from transformers.testing_utils import require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extra... | 717 |
'''simple docstring'''
A : List[str] = '\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 : List[str] = [{'type': 'code', 'content': INS... | 273 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import _LazyModule
SCREAMING_SNAKE_CASE = {'processing_wav2vec2_with_lm': ['Wav2Vec2ProcessorWithLM']}
if TYPE_CHECKING:
from .processing_wavaveca_with_lm import WavaVecaProcessorWithLM
else:
import sys
SC... | 94 |
'''simple docstring'''
import unittest
from dataclasses import dataclass
import pytest
from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict
@dataclass
class UpperCAmelCase_ ( __... | 94 | 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, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipel... | 703 |
"""simple docstring"""
import unittest
import torch
from torch import nn
from accelerate.test_utils import require_cuda
from accelerate.utils.memory import find_executable_batch_size, release_memory
def UpperCAmelCase ( ):
'''simple docstring'''
raise RuntimeError('CUDA out of me... | 133 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__lowerCAmelCase = {
'configuration_roformer': ['ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', '... | 201 |
import torch
from diffusers import DDPMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class lowerCamelCase ( __lowerCamelCase ):
UpperCamelCase_ : int = (DDPMParallelScheduler,)
def snake_case__ ( self :Any , **lowercase :str )... | 201 | 1 |
from itertools import product
def __lowerCamelCase ( _lowerCAmelCase , _lowerCAmelCase ) -> list[int]:
_UpperCAmelCase = sides_number
_UpperCAmelCase = max_face_number * dice_number
_UpperCAmelCase = [0] * (max_total + 1)
_UpperCAmelCase = 1
_UpperCAmelCase = rang... | 719 |
from typing import List, Optional, Tuple, Union
import torch
from torch import nn
from torch.nn import CrossEntropyLoss
from ... import AutoBackbone
from ...modeling_outputs import SemanticSegmenterOutput
from ...modeling_utils import PreTrainedModel
from ...utils import add_start_docstrings, add_start... | 129 | 0 |
import gc
import unittest
import numpy as np
import torch
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps
from ..pipeline_param... | 67 | '''simple docstring'''
import os
import sys
import tempfile
import torch
from .state import AcceleratorState
from .utils import PrecisionType, PrepareForLaunch, is_mps_available, patch_environment
def __UpperCamelCase( _A : Any , _A : List[str]=() , _A : List[str]=None ... | 614 | 0 |
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_modeling_t... | 713 |
def _lowercase ( lowercase__ , lowercase__ ):
__lowerCAmelCase : Union[str, Any] = len(lowercase__ )
__lowerCAmelCase : Any = len(lowercase__ )
__lowerCAmelCase : str = [[False for _ in range(m + 1 )] for _ in range(n + 1 )]
__low... | 583 | 0 |
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import flax
import jax.numpy as jnp
from jax import random
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedulerMixin
@flax.struct.dataclass
... | 590 |
from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow
from ..bert.test_tokenization_bert import BertTokenizationTest
@require_tokenizers
class _lowerCamelCase ( UpperCamelCase ):
"""simple docstring"""
... | 590 | 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 = logging.get_logger(__name__)
_UpperCAmelCase = {
"facebook/xmod-base": "... | 706 |
from __future__ import annotations
def _lowerCamelCase ( _a , _a , _a ):
"""simple docstring"""
if (voltage, current, resistance).count(0 ) != 1:
raise ValueError('''One and only one argument must be 0''' )
if resistance < 0:
raise ValueError('''Resistance cannot be... | 297 | 0 |
"""simple docstring"""
from typing import List, Optional
import numpy as np
from ...processing_utils import ProcessorMixin
from ...utils import to_numpy
class __magic_name__ ( SCREAMING_SNAKE_CASE__ ):
UpperCamelCase_ = '''EncodecFeatureExtractor'''
UpperCamelCase_ = ('''T5T... | 353 |
"""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
A__ : List[Any] = logging.get_logger(__name... | 353 | 1 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Callable
def lowerCamelCase__ ( a , a , a , a = 100 , ):
__snake_case = x_start
__snake_case = fnc(a )
__snake_case = 0.0
for _ in range... | 427 |
'''simple docstring'''
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
... | 427 | 1 |
'''simple docstring'''
import os
import random
import sys
from . import cryptomath_module as cryptomath
from . import rabin_miller
lowercase__ : Dict = 3
def a__ ( lowercase : Dict ) -> int:
"""simple docstring"""
print('''Generating primitive root... | 98 |
import unittest
from datasets import load_dataset
from transformers.pipelines import pipeline
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_torch, slow
@is_pipeline_test
@require_torch
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
@require_torch
... | 319 | 0 |
print((lambda quine: quine % quine)("""print((lambda quine: quine %% quine)(%r))"""))
| 25 |
import argparse
import fairseq
import torch
from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging
logging.set_verbosity_info()
__lowerCamelCase : int = logging.get_logger(__name__)
__lowerCamelCase : int = {
"""post_extract_proj"... | 25 | 1 |
'''simple docstring'''
from __future__ import annotations
import math
def _UpperCamelCase (_lowerCamelCase : int )-> bool:
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number ... | 24 | import random
import unittest
import numpy as np
import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax
if is_flax_available():
import os
import jax.numpy as jnp
from jax import jit
from transformers imp... | 524 | 0 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A = logging.get_logger(__name__)
A = {
'BAAI/AltCLIP': 'https://huggingface.co/BAAI/AltCLIP/resolve/main/conf... | 713 |
'''simple docstring'''
from manim import *
class __snake_case ( a__):
def UpperCAmelCase_ ( self ):
"""simple docstring"""
lowerCamelCase : Optional[int] = Rectangle(height=0.5, width=0.5 )
lowerCamelCase ... | 449 | 0 |
import argparse
import struct
import unittest
class _lowerCAmelCase :
def __init__( self : Union[str, Any] , __snake_case : bytes ):
lowerCamelCase :Any = data
# Initialize hash values
lowerCamelCase :List[Any] = [
... | 166 | import argparse
import torch
from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging
logging.set_verbosity_info()
def _lowerCamelCase ( a_ : Optional[int] , a_ : Optional[Any] , a_ : List[st... | 166 | 1 |
from __future__ import annotations
from collections.abc import Iterator
class lowerCamelCase :
"""simple docstring"""
def __init__( self , __UpperCamelCase ):
A_ = value
A_ = None
A_ = None
class lo... | 608 |
def lowerCAmelCase ( snake_case__ : list )-> list:
if len(snake_case__ ) <= 1:
return lst
A_ = 1
while i < len(snake_case__ ):
if lst[i - 1] <= lst[i]:
i += 1
else:
A_ , A_ = lst[i], lst[i - 1]
... | 608 | 1 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_convbert import ConvBertTokenizer
lowercase : List[Any] = l... | 649 |
from __future__ import annotations
from math import ceil, floor, sqrt
def _lowerCamelCase ( __A : int = 2_000_000 ) -> int:
_UpperCAmelCase : list[int] = [0]
_UpperCAmelCase : int
for idx in range(1 , ceil(sqrt(target * 2 ) * 1.1 ... | 485 | 0 |
from collections import defaultdict
def snake_case ( UpperCAmelCase : int ):
A = 1
A = True
for v in tree[start]:
if v not in visited:
ret += dfs(UpperCAmelCase )
if ret % 2 == 0:
cuts.append(UpperCAmelCase )
return ret
... | 720 |
from ...configuration_utils import PretrainedConfig
class UpperCamelCase ( snake_case__ ):
"""simple docstring"""
snake_case = "bert-generation"
def __init__( self : Tuple ,_SCREAMING_SNAKE_CASE : Tuple=5_0_3_5_8 ,_SCREAMING_SNAKE_CASE : str=1_0_2_4 ... | 110 | 0 |
"""simple docstring"""
class lowerCAmelCase_ :
'''simple docstring'''
def __init__( self : Optional[int] ) -> List[str]:
A = 0
A = 0
A = {}
def _SCREAMING_SNAKE_CASE ( self : int ,A_ : Optional[Any] ) -> int:
if vertex... | 91 | import unittest
import numpy as np
from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
expand_dims,
flatten_dict,
is_flax_available,
is_tf_available,
is_torch_available,
reshape,
squeeze,
transpose,
)
if is_flax_available():... | 576 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__lowercase = {
"""configuration_transfo_xl""": ["""TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TransfoXLConfig"""],
"""tokenization_transfo_xl""": ["""Transfo... | 563 |
from __future__ import annotations
import pandas as pd
def _lowerCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
'''simple docstring'''
A_ = [0] * no_of_processes
A_ = [0] * no_of_processes
... | 563 | 1 |
import torch
from diffusers import DDIMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class a (_lowerCAmelCase ):
"""simple docstring"""
__UpperCAmelCase : List[str] = (DDIMParallelScheduler,)
__UpperCAmelCase : Tuple = (("... | 81 | """simple docstring"""
from collections import defaultdict
from pathlib import Path
import pandas as pd
from rouge_cli import calculate_rouge_path
from utils import calculate_rouge
__A = [
'''Prosecutor: "No videos were used in the crash investigation" German papers say they saw a cell phone video of t... | 646 | 0 |
'''simple docstring'''
from random import shuffle
import tensorflow as tf
from numpy import array
def A_ ( snake_case , snake_case ):
SCREAMING_SNAKE_CASE:Union[str, Any] = int(snake_case )
assert noofclusters < len(snake_case )
# Find out the dimensionalit... | 465 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class _snake_case ( metaclass=_a ):
_A : Any = ['''torch''', '''torchsde''']
def __init__( self : Any ,*SCREAMING_SNAKE_CASE__ : int ,**SCREAMING_SNAKE_CASE__ ... | 465 | 1 |
from __future__ import annotations
def a__ ( lowercase__ ):
'''simple docstring'''
UpperCAmelCase_ =len(lowercase__ ) // 2
# choose the middle 3 elements
UpperCAmelCase_ =lst[m - 1 : m + 2]
# if middle element is peak
... | 54 |
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_configuration_common impo... | 54 | 1 |
def A_ ( snake_case : int , snake_case : int ) -> int:
'''simple docstring'''
return int(input_a == input_a == 0 )
def A_ ( ) -> None:
'''simple docstring'''
print('''Truth Table of NOR Gate:''' )
print('''| Input 1 | Inp... | 451 |
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, Dist... | 451 | 1 |
def UpperCamelCase ( snake_case__ : str , snake_case__ : str ) -> bool:
UpperCamelCase : List[str] = len(snake_case__ )
UpperCamelCase : Any = len(snake_case__ )
UpperCamelCase : int = [[False f... | 40 | """simple docstring"""
import warnings
from ...utils import logging
from .image_processing_yolos import YolosImageProcessor
__A = logging.get_logger(__name__)
class UpperCAmelCase (_UpperCAmelCase ):
"""simple docstring"""
def __init__( self , *_UpperCAmelCase ... | 586 | 0 |
from __future__ import annotations
lowerCamelCase__ = '''Muhammad Umer Farooq'''
lowerCamelCase__ = '''MIT'''
lowerCamelCase__ = '''1.0.0'''
lowerCamelCase__ = '''Muhammad Umer Farooq'''
lowerCamelCase__ = '''contact@muhammadumerfarooq.me'''
lowerCamelCase__ = '''Alpha'''
imp... | 82 | import argparse
import os
import torch
from transformers import (
XLNetConfig,
XLNetForQuestionAnswering,
XLNetForSequenceClassification,
XLNetLMHeadModel,
load_tf_weights_in_xlnet,
)
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
lowerCamelCase__ = {
'''cola''': 2,... | 82 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable
_UpperCamelCase : Union[str, Any] = {
'''configuration_gpt_neox_japanese''': ['''GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE... | 599 |
'''simple docstring'''
import re
from pathlib import Path
from unittest import TestCase
import pytest
@pytest.mark.integration
class SCREAMING_SNAKE_CASE__ ( _UpperCamelCase ):
def A ( self : Optional[Any] , a_ : str ):
""... | 69 | 0 |
import inspect
import unittest
import warnings
from math import ceil, floor
from transformers import LevitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, requ... | 25 |
import argparse
import fairseq
import torch
from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging
logging.set_verbosity_info()
__lowerCamelCase : int = logging.get_logger(__name__)
__lowerCamelCase : int = {
"""post_extract_proj"... | 25 | 1 |
import os
from pathlib import Path
from unittest.mock import patch
import pytest
import zstandard as zstd
from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
OfflineModeIsEnabled,
cached_path,
fsspec_get,
fsspec_head,
ftp_get,
ftp_head,
... | 45 | '''simple docstring'''
import os
import sys
import tempfile
import torch
from .state import AcceleratorState
from .utils import PrecisionType, PrepareForLaunch, is_mps_available, patch_environment
def __UpperCamelCase( _A : Any , _A : List[str]=() , _A : List[str]=None ... | 614 | 0 |
'''simple docstring'''
from __future__ import annotations
from itertools import permutations
from random import randint
from timeit import repeat
def _SCREAMING_SNAKE_CASE ( ):
_lowercase = [randint(-1000 , 1000 ) for i in range(10 )]
_lowercase = ... | 719 |
'''simple docstring'''
from collections import OrderedDict
from typing import List, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_lowerCamelCase = logging.get_logger(__name__)
_lowerCamelCase ... | 572 | 0 |
import pytest
import datasets
# Import fixture modules as plugins
__lowerCamelCase : List[Any] = ["tests.fixtures.files", "tests.fixtures.hub", "tests.fixtures.fsspec"]
def SCREAMING_SNAKE_CASE__ ( snake_case_, snake_case_ ) -> List[Any]:
# Mark ... | 416 |
from typing import Any
class a :
def __init__( self , __UpperCamelCase )-> List[str]:
'''simple docstring'''
A__ : Union[str, Any] =data
A__ : Tuple =None
def __repr__( self )-> str:
'''simple docs... | 416 | 1 |
import numpy as np
def lowercase ( SCREAMING_SNAKE_CASE__ : np.ndarray ) -> np.ndarray:
return 1 / (1 + np.exp(-vector ))
def lowercase ( SCREAMING_SNAKE_CASE__ : np.ndarray ) -> np.ndarray:
return vector * sigmoid(SCREAMING_SNAKE_CASE... | 198 |
def lowercase ( SCREAMING_SNAKE_CASE__ : int = 1_000 ) -> int:
_snake_case , _snake_case : str = 1, 1
_snake_case : List[Any] = 2
while True:
_snake_case : Union[str, Any] = 0
_snake_case : int = fa + fa
_snake_case , ... | 198 | 1 |
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.data import DataLoader, RandomS... | 216 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__A = {
"configuration_pix2struct": [
"PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP",
"Pix2StructConfig",
"Pix2StructTextConfig",
... | 59 | 0 |
import os
import sys
import tempfile
import torch
from .state import AcceleratorState
from .utils import PrecisionType, PrepareForLaunch, is_mps_available, patch_environment
def A ( _lowerCamelCase , _lowerCamelCase=() , _lowerCamelCase=None , _lowerCamelCase="no... | 704 |
import requests
from bsa import BeautifulSoup
def A ( _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : List[str] = BeautifulSoup(requests.get(_lowerCamelCase , params=_lowerCamelCase ).content ... | 658 | 0 |
import os
from glob import glob
import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn
from transformers import CLIPModel, CL... | 598 |
from math import log
from scipy.constants import Boltzmann, physical_constants
_a : List[str] = 300 # TEMPERATURE (unit = K)
def a_ ( __magic_name__ , __magic_name__ , __magic_name__ , ) -> float:
"""simple docstring"""
... | 598 | 1 |
from dataclasses import dataclass
from typing import Tuple
import numpy as np
import torch
@dataclass
class lowerCAmelCase__:
'''simple docstring'''
__snake_case = 42 # [batch_size x 3]
__snake_case = 42 # [batch_size x 3]
__snake_case = 42 # [batc... | 381 |
import copy
import os
from typing import TYPE_CHECKING, List, Union
if TYPE_CHECKING:
pass
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__ =logging.get_logger(__name__)
UpperCamelCase__ ={
'kakaobrain/align-base': 'https://huggingface.co... | 381 | 1 |
def _UpperCAmelCase (UpperCamelCase_ : list , UpperCamelCase_ : list , UpperCamelCase_ : int , UpperCamelCase_ : int , UpperCamelCase_ : int ):
'''simple docstring'''
if index == number_of_items:
return 0
_lowerCAmelCase : Optional[A... | 429 |
from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors
def _UpperCAmelCase (UpperCamelCase_ : int ):
'''simple docstring'''
_lowerCAmelCase : Optional[Any] = prime_factors(UpperCamelCase_ )
if is_square_free(UpperCame... | 429 | 1 |
import os
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from huggingface_hub.file_download import http_get
from requests.exceptions import HTTPError
from transformers import (
AlbertTokenizer,
AutoTokeni... | 240 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_UpperCAmelCase = {'configuration_wavlm': ['WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'WavLMConfig']}
try:
if not is_torch_available():
raise OptionalDependencyNotA... | 240 | 1 |
class _A : # Public class to implement a graph
'''simple docstring'''
def __init__( self : Union[str, Any] , lowerCamelCase : int , lowerCamelCase : int , lowerCamelCase : list[list[bool]] ):
'''simple docstring'''
... | 402 |
from math import sqrt
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format o... | 402 | 1 |
import argparse
import json
import os
import fairseq
import torch
from torch import nn
from transformers import (
SpeechaTextaConfig,
SpeechaTextaForCausalLM,
SpeechaTextaTokenizer,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaV... | 715 |
import numpy as np
class _UpperCamelCase :
'''simple docstring'''
def __init__( self : Dict ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[str] = (0, 0)
SCREAMING_SNAKE_CASE : Any = None
SCREAMING_SN... | 193 | 0 |
'''simple docstring'''
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
fr... | 22 |
import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
lowerCAmelCase : Union[str, Any] = logging.get_logger(__name__)
class _A ( __magic_name__):
def __init__( self , *_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE ):
... | 511 | 0 |
def lowerCamelCase_ ( UpperCamelCase_ ):
_a : Any = 1
for i in range(1 , num + 1 ):
fact *= i
return fact
def lowerCamelCase_ ( UpperCamelCase_ ):
_a : List[Any] = 0
while number > 0:
_a : Optional... | 706 |
import uuid
from typing import Any, Dict, List, Optional, Union
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
if is_torch_available():
import torch
__UpperCAmelCase ... | 249 | 0 |
'''simple docstring'''
def lowercase__( _UpperCamelCase : int , _UpperCamelCase : list[int] , _UpperCamelCase : int )-> int:
"""simple docstring"""
def count_of_possible_combinations(_UpperCamelCase : int ) -> int:
if target < 0:
return 0
... | 138 |
'''simple docstring'''
import unittest
from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
snake_case_ : Any = get_tests_dir('''fixtures/test... | 138 | 1 |
from typing import Callable, List, Optional, Union
import PIL
import torch
from transformers import (
CLIPImageProcessor,
CLIPSegForImageSegmentation,
CLIPSegProcessor,
CLIPTextModel,
CLIPTokenizer,
)
from diffusers import DiffusionPipeline
from diffusers.configuration_utils import FrozenDic... | 371 |
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
_UpperCAmelCase = logging.get_logger(__name__)
_UpperCAmelCase =... | 371 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
_lowerCAmelCase = logging.get_logger(__name__)
_lowerCAmelCase = {
"go... | 161 |
'''simple docstring'''
import fire
from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer
def _lowerCAmelCase ( lowercase : str , lowercase : str , **lowercase : Tuple ) ->Tuple:
"""simple docstring"""
lowercase... | 161 | 1 |
import json
import os
import unittest
from transformers.models.gptsan_japanese.tokenization_gptsan_japanese import (
VOCAB_FILES_NAMES,
GPTSanJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers... | 716 | import numpy as np
import qiskit
def snake_case ( snake_case__ :int = 8 , snake_case__ :int | None = None) -> str:
_A = np.random.default_rng(seed=snake_case__)
# Roughly 25% of the qubits will contribute to the key.
# So we take more than we ... | 83 | 0 |
'''simple docstring'''
def __A ( a_ : int ,a_ : float ,a_ : float ):
return round(float(moles / volume ) * nfactor )
def __A ( a_ : float ,a_ : float ,a_ : float ):
return round(float((moles * 0.0_8_2_1 * temperature) / (volume) ) ... | 525 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import _LazyModule
lowerCAmelCase = {"""tokenization_wav2vec2_phoneme""": ["""Wav2Vec2PhonemeCTCTokenizer"""]}
if TYPE_CHECKING:
from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
import ... | 525 | 1 |
'''simple docstring'''
from math import ceil
from typing import List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import BatchFeature, SequenceFeatureExtractor
from ...utils import TensorType, logging
snake_c... | 706 |
'''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
snake_case = logging.getLogger(__name__)
snake_case = ... | 568 | 0 |
'''simple docstring'''
def lowercase_ ( _lowercase ) -> Dict:
'''simple docstring'''
lowerCamelCase_ : Union[str, Any] = 0
lowerCamelCase_ : str = len(_lowercase )
for i in range(n - 1 ):
for j in range(i + 1 , _lowercase ):
... | 422 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowercase : Optional[int] = logging.get_logger(__name__)
__lowercase : Optional[int] = {
'''studio-ousia/luke-base''': '''https://huggingface.co/studio-ousia/luke-base/resolve/... | 422 | 1 |
'''simple docstring'''
from typing import Any, Dict, List, Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from ..image_utils import load_image
if is_torch_availa... | 88 |
'''simple docstring'''
import unittest
from datasets import load_dataset
from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class __SCREAMING_SNAKE_CASE ( lowercase_... | 88 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowerCAmelCase__ : Optional[int] ={
'configuration_canine': ['CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'CanineConfig'],
'tokenization_canine'... | 101 |
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_available... | 392 | 0 |
'''simple docstring'''
import os
import tempfile
import unittest
from transformers import DistilBertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelT... | 425 |
'''simple docstring'''
def _A ( A ) -> bool:
return str(A ) == str(A )[::-1]
def _A ( A ) -> int:
return int(A ) + int(str(A )[::-1] )
def _A ( A = 1_0_0_0_0 ) -> int:
lowercase : List[Any] ... | 425 | 1 |
import json
import os
import unittest
from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class __lowercase (snake_case__ , unittest.TestCase ):
_UpperCamelCase = CTRLTokenizer
_UpperCa... | 492 |
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartaaTokenizer, MBartaaTokenizerFast, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
requ... | 550 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__SCREAMING_SNAKE_CASE = {
'configuration_bloom': ['BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BloomConfig', 'BloomOnnxConfig'],
}
try:
if not is_tokenizers_avai... | 153 |
from typing import Dict, Iterable, 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... | 153 | 1 |
import warnings
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...ut... | 74 |
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 Tokeniz... | 84 | 0 |
'''simple docstring'''
import argparse
import os
import torch
from transformers import FlavaConfig, FlavaForPreTraining
from transformers.models.flava.convert_dalle_to_flava_codebook import convert_dalle_checkpoint
def __UpperCamelCase ( __lowerCamelCase : Optional[int] ) -> Dict:
... | 276 |
'''simple docstring'''
lowercase__ = 65_521
def __UpperCamelCase ( __lowerCamelCase : str ) -> int:
'''simple docstring'''
_a = 1
_a = 0
for plain_chr in plain_text:
_a = (a + ord(__lowerCamelCase )) % MOD_ADLER
... | 276 | 1 |
import gc
import random
import unittest
import torch
from diffusers import (
IFImgaImgPipeline,
IFImgaImgSuperResolutionPipeline,
IFInpaintingPipeline,
IFInpaintingSuperResolutionPipeline,
IFPipeline,
IFSuperResolutionPipeline,
)
from diffusers.models.attention_processor import AttnAddedK... | 105 | import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils... | 544 | 0 |
import math
import flax.linen as nn
import jax.numpy as jnp
def snake_case__ ( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ = 1 , lowerCamelCase_ = 1 , lowerCamelCase_ = 1.0E4 , lowerCamelCase_ = False , lowerCamelCase_ = 1.0 ... | 423 |
import heapq
import sys
import numpy as np
lowercase : Optional[int] = tuple[int, int]
class __lowercase :
"""simple docstring"""
def __init__( self ) -> List[str]:
A : List[str] = []
A : s... | 423 | 1 |
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