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 |
|---|---|---|---|---|
from __future__ import annotations
import math
def lowerCAmelCase_ (lowercase__ : float , lowercase__ : int ) -> float:
'''simple docstring'''
lowerCAmelCase__ = u
for i in range(1 , lowercase__ ):
lowerCAmelCase__... | 668 |
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,
BartForSequenceClassification,
D... | 668 | 1 |
import argparse
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import (
RobertaTokenizer,
TrOCRConfig,
TrOCRForCausalLM,
TrOCRProcessor,
VisionEncoderDecoderModel,
ViTConfig,
ViTImageProcessor,
ViTModel,
)
from transform... | 716 | from typing import Dict, List, Optional, Union
import numpy as np
from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy
_lowerCamelCase : Optional[Any] = logging.get_logger(__na... | 647 | 0 |
'''simple docstring'''
import argparse
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
__A : Any ... | 394 |
'''simple docstring'''
class _UpperCamelCase :
'''simple docstring'''
def __init__( self , _a ):
"""simple docstring"""
# we need a list not a string, so do something to change the type
a__ = arr.split(',' )
... | 394 | 1 |
"""simple docstring"""
from typing import List, Optional, Union
import numpy as np
import tensorflow as tf
from .utils import logging
_UpperCamelCase = logging.get_logger(__name__)
def _a ( _snake_case ):
"""simple docstring"""
if isinstance(_snake_case ,... | 74 |
"""simple docstring"""
import unittest
from transformers.utils.backbone_utils import (
BackboneMixin,
get_aligned_output_features_output_indices,
verify_out_features_out_indices,
)
class lowerCamelCase__ ( unittest.TestCase ):
def _UpperCamelCase ( ... | 74 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__snake_case :List[str] =logging.get_logger(__name__)
__snake_case :int ={'openai-gpt': 'https://huggingface.co/openai-gpt/resolve/main/config.json'}
class lowerCAmelCase__ ( _lowerCamelCase ... | 106 |
'''simple docstring'''
UpperCamelCase ="Input must be a string of 8 numbers plus letter"
UpperCamelCase ="TRWAGMYFPDXBNJZSQVHLCKE"
def snake_case ( a_ : str ) -> bool:
"""simple docstring"""
if not isinstance(a_ , a_ ):
... | 208 | 0 |
"""simple docstring"""
import logging
import os
import sys
import warnings
from dataclasses import dataclass, field
from random import randint
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import DatasetDict, load_dataset
import transformers
from transformers import ... | 705 |
"""simple docstring"""
import warnings
from ..trainer import Trainer
from ..utils import logging
__SCREAMING_SNAKE_CASE : Dict = logging.get_logger(__name__)
class lowerCamelCase_( A__ ):
'''simple docstring'''
def __init__( self , lowerCamelCase__... | 623 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
A : Optional[Any] = {
"configuration_mobilebert": [
"MOBILEBERT_PRETRAINED_C... | 636 |
"""simple docstring"""
import baseaa
def _lowerCamelCase ( _UpperCamelCase ):
'''simple docstring'''
return baseaa.baaencode(string.encode("utf-8" ) )
def _lowerCamelCase ( _UpperCamelCase ):
'''simple docstring'''
return baseaa.baadecode(_UpperCamelCase ).decode("utf-... | 636 | 1 |
'''simple docstring'''
import torch
from diffusers import StableDiffusionPipeline
_UpperCamelCase = """path-to-your-trained-model"""
_UpperCamelCase = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to("""cuda""")
_UpperCamelCase = """A phot... | 211 |
'''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/l... | 211 | 1 |
import torch
from diffusers import DDPMScheduler
from .test_schedulers import SchedulerCommonTest
class A( __snake_case ):
'''simple docstring'''
UpperCamelCase = (DDPMScheduler,)
def a__ ( self : List[str] , **A_ : str ) -> ... | 70 |
"""simple docstring"""
from __future__ import annotations
import math
def a__ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) -> int:
if depth < 0:
r... | 346 | 0 |
'''simple docstring'''
import multiprocessing
import os
from typing import BinaryIO, Optional, Union
import fsspec
from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import Nest... | 624 |
'''simple docstring'''
import hashlib
import unittest
from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
... | 624 | 1 |
'''simple docstring'''
from typing import Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
from ..mod... | 41 |
'''simple docstring'''
import string
def _lowerCAmelCase ( lowerCamelCase_ : str ):
__lowercase = ''''''
for i in sequence:
__lowercase = ord(lowerCamelCase_ )
if 6_5 <= extract <= 9_0:
output += chr(1_5_5 - extract ... | 502 | 0 |
"""simple docstring"""
from argparse import ArgumentParser
from datasets.commands.convert import ConvertCommand
from datasets.commands.dummy_data import DummyDataCommand
from datasets.commands.env import EnvironmentCommand
from datasets.commands.run_beam import RunBeamCommand
from datasets.commands.test import TestCom... | 703 | """simple docstring"""
from __future__ import annotations
from collections.abc import Iterator
from typing import Generic, TypeVar
lowercase__ = TypeVar('T')
class __snake_case ( Generic[T] ):
def __init__( self , lowercase) -> List[Any]:
'''simple docstring'... | 217 | 0 |
"""simple docstring"""
import torch
from torch import nn
class UpperCAmelCase ( nn.Module ):
def __init__( self : List[str] , __lowerCamelCase : Dict , __lowerCamelCase : str , __lowerCamelCase : Optional[Any] , ... | 103 |
import unittest
from diffusers import FlaxAutoencoderKL
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax
from .test_modeling_common_flax import FlaxModelTesterMixin
if is_flax_available():
import jax
@require_flax
class __low... | 141 | 0 |
"""simple docstring"""
import argparse
import gc
import json
import os
import shutil
import warnings
import torch
from transformers import LlamaConfig, LlamaForCausalLM, LlamaTokenizer
try:
from transformers import LlamaTokenizerFast
except ImportError as e:
warnings.warn(e)
... | 712 |
"""simple docstring"""
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 transfo... | 28 | 0 |
import warnings
from ...utils import logging
from .image_processing_deit import DeiTImageProcessor
lowerCAmelCase_ = logging.get_logger(__name__)
class _lowerCAmelCase ( UpperCAmelCase_ ):
'''simple docstring'''
def __init__( self : Union[str, Any] , *... | 411 |
import math
from enum import Enum
from typing import Optional, Union
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from .utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
class _lowerCAmelCase ( UpperCAmelCase_ ):
'''simple ... | 411 | 1 |
'''simple docstring'''
from typing import Any, Dict, Optional
import torch
import torch.nn.functional as F
from torch import nn
from ..utils import maybe_allow_in_graph
from .activations import get_activation
from .attention_processor import Attention
from .embeddings import CombinedTimestepLabelEmbeddings
... | 715 | '''simple docstring'''
import argparse
import logging
import os
from datetime import datetime
import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from tqdm import tqdm
from transformers import GPTaLMHeadModel
UpperCamelCase_ = loggin... | 320 | 0 |
import math
import os
import unittest
from transformers import MegatronBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTest... | 611 |
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_ = {
"andreasmadsen/efficient_mlm_m0.40": (
... | 611 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {
"weiweishi/roc-bert-base-zh": "https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json",
}
class __lowerCAmelCase ( ... | 708 |
import os
# Precomputes a list of the 100 first triangular numbers
UpperCAmelCase__ = [int(0.5 * n * (n + 1)) for n in range(1, 101)]
def A ( ) -> List[str]:
'''simple docstring'''
_UpperCAmelCase = os.path.dirname(os.path.realpath(_UpperCAmelCase ) )
_Upper... | 639 | 0 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision... | 400 |
'''simple docstring'''
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.text import TextDatasetReader
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def UpperCamelCase_( snake_case : Union[str, Any] ... | 400 | 1 |
import json
import os
import unittest
from transformers import AutoTokenizer, GPTaTokenizer, GPTaTokenizerFast
from transformers.models.gpta.tokenization_gpta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_to... | 712 |
from manim import *
class __UpperCamelCase ( _lowercase ):
"""simple docstring"""
def _UpperCAmelCase ( self ) -> Dict:
a__ = Rectangle(height=0.5 , width=0.5 )
a__ = Rectangle(height=0.46 , width=0.46 ).set_stroke(width=0 )
a__ ... | 148 | 0 |
from ..utils import is_flax_available, is_torch_available
if is_torch_available():
from .autoencoder_kl import AutoencoderKL
from .controlnet import ControlNetModel
from .dual_transformer_ad import DualTransformeraDModel
from .modeling_utils import ModelMixin
from .prior_transformer import PriorTransfor... | 175 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
"""google/canine-s""": """https://huggingface.co/google/canine-s/resolve/main/config.json""",
# See all CANINE models at https://huggingface.co/models?filter... | 175 | 1 |
'''simple docstring'''
import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from ... | 399 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCAmelCase = logging.get_logger(__name__)
_lowerCAmelCase = {
'''funnel-transformer/small''': '''https://huggingface.co/funnel-transformer/small/resolve/m... | 399 | 1 |
from math import factorial
__lowerCamelCase : List[str] = {str(d): factorial(d) for d in range(10)}
def lowerCamelCase_(lowerCamelCase_ ) -> int:
return sum(DIGIT_FACTORIAL[d] for d in str(lowerCamelCase_ ) )
def lowerCamelCase_() -> int:
UpperCAmelCase ... | 323 |
import os
from datetime import datetime as dt
from github import Github
__lowerCamelCase : Optional[int] = [
"good first issue",
"good second issue",
"good difficult issue",
"enhancement",
"new pipeline/model",
"new scheduler",
"wip",
]
def lowerCamelCase_() -> L... | 323 | 1 |
'''simple docstring'''
from __future__ import annotations
def a__ ( _SCREAMING_SNAKE_CASE : int ) -> list[int]:
"""simple docstring"""
UpperCAmelCase_ : Tuple = 2
UpperCAmelCase_ : Dict = []
while i * i <= n:
if n % i:
i... | 323 |
'''simple docstring'''
from __future__ import annotations
import json
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
_lowerCamelCase = {"""UserAgent""": UserAgent().random}
def a__ ( _SCREAMING_SNAKE_CASE : Any ) -> dict:... | 323 | 1 |
def __UpperCamelCase ( _lowerCAmelCase ) -> int:
"""simple docstring"""
A : int = 0
while num > 0:
digit_sum += num % 10
num //= 10
return digit_sum
def __UpperCamelCase ( _lowerCAmelCase = 100 ) -> int:
"""s... | 662 |
from typing import TYPE_CHECKING
from ....utils import _LazyModule
SCREAMING_SNAKE_CASE_:int = {"""tokenization_tapex""": ["""TapexTokenizer"""]}
if TYPE_CHECKING:
from .tokenization_tapex import TapexTokenizer
else:
import sys
SCREAMING_SNAKE_CASE_:Dict = _LazyModule(__name__, global... | 662 | 1 |
import gc
import threading
import time
import psutil
import torch
class UpperCAmelCase :
def __init__( self : str ):
"""simple docstring"""
UpperCamelCase = psutil.Process()
UpperCamelCase = ... | 705 |
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.pipelines.kandinsky.text_en... | 181 | 0 |
"""simple docstring"""
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import VideoMAEConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from tran... | 29 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import is_flax_available
from transformers.testing_utils import require_flax
from ..test_modeling_flax_common import ids_tensor
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.generation i... | 29 | 1 |
import dataclasses
import json
import sys
import types
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, ArgumentTypeError
from copy import copy
from enum import Enum
from inspect import isclass
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, List, Literal, NewType, Option... | 467 |
# Note: if you intend to run this script make sure you look under scripts/fsmt/
# to locate the appropriate script to do the work correctly. There is a set of scripts to:
# - download and prepare data and run the conversion script
# - perform eval to get the best hparam into the config
# - generate model_cards - usef... | 467 | 1 |
"""simple docstring"""
import os
import socket
from contextlib import contextmanager
import torch
from ..commands.config.default import write_basic_config # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_a... | 19 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Image
from .base import TaskTemplate
@dataclass(frozen=__SCREAMING_SNAKE_CASE )
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
snake... | 55 | 0 |
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class UpperCAmelCase__ ( __snake_case ... | 716 |
import json
import os
import unittest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@requi... | 332 | 0 |
import gc
import unittest
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
PriorTransformer,
StableUnCLIPPipeline,
UNetaDConditionModel,
)
from diffusers.p... | 80 |
"""simple docstring"""
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
a : ... | 218 | 0 |
"""simple docstring"""
import warnings
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a_ : Tuple = logging.get_logger(__name__)
a_ ... | 721 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .t... | 263 | 0 |
from datetime import datetime as dt
import os
from github import Github
A__ : Optional[Any] = [
'''good first issue''',
'''good second issue''',
'''good difficult issue''',
'''feature request''',
'''new model''',
'''wip''',
]
def a ( ):
'''si... | 183 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mvp import ... | 594 | 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_video_inputs
if is_torch_ava... | 715 |
"""simple docstring"""
from __future__ import annotations
__lowerCAmelCase : Optional[int] = 8.988E9 # units = N * m^s * C^-2
def __snake_case ( UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> dict[str, float]:
"""simple docstring"""
... | 158 | 0 |
import logging
import os
import quant_trainer
import torch
from torch.utils.data import DataLoader
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput
UpperCamelCase__ = logging.getLogger(__name__)
if is_torch_tpu_available(check_device... | 619 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase = logging.get_logger(__name__)
_UpperCAmelCase = {
'google/pegasus-large': 'https://huggingface.co/google/pegasus-large/resolve/main/config.json',
# See all PEGASUS models at https://huggi... | 504 | 0 |
'''simple docstring'''
import inspect
import tempfile
import unittest
from huggingface_hub import hf_hub_download
from transformers import is_torch_available
from transformers.testing_utils import is_flaky, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_mo... | 705 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__lowerCamelCase : List[str] = logging.get_logger(__name__)
__lowerCamelCase : str = {
... | 459 | 0 |
from itertools import product
def UpperCAmelCase_ ( __UpperCamelCase, __UpperCamelCase ):
SCREAMING_SNAKE_CASE__ =sides_number
SCREAMING_SNAKE_CASE__ =max_face_number * dice_number
SCREAMING_SNAKE_CASE__ =[0] * (max_total + 1)
SCREAMING_SNAKE_CAS... | 151 |
import tempfile
import unittest
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
is_torch_available,
require_optimum,
require_torch,
slow,
)
if is_torch_available():
import torch
@require_torch
@require_optimum
@slow
class __a ( un... | 151 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
a = {
'configuration_poolformer': [
'POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'PoolFormerConfig',
'PoolFormerOnnxConfig',
]
}
try:... | 716 |
from dataclasses import asdict, dataclass
from typing import Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a = logging.get_logger(__name__)
# TODO Update this
a = {
'facebook/esm-1b': 'https://huggingface.co/facebook/esm-1b/resolve/main/config.j... | 650 | 0 |
'''simple docstring'''
def A ( UpperCamelCase_ : int ) -> str:
'''simple docstring'''
lowerCAmelCase__ = int(UpperCamelCase_ )
if decimal in (0, 1): # Exit cases for the recursion
return str(UpperCamelCase_ )
lowerCAmelCase__ ,lowerCAmelCase__... | 48 |
from .testing import (
are_the_same_tensors,
execute_subprocess_async,
require_bnb,
require_cpu,
require_cuda,
require_huggingface_suite,
require_mps,
require_multi_gpu,
require_multi_xpu,
require_safetensors,
require_single_gpu,
require_single_xpu,
require_torch_... | 593 | 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
a : List[str] = logging.get_logger(__n... | 716 |
'''simple docstring'''
import inspect
import warnings
from typing import Any, Dict, Optional, Union
from packaging import version
def __magic_name__ ( *__UpperCAmelCase, __UpperCAmelCase = None, __UpperCAmelCase=True, __UpperCAmelCase=2 ) -> int:
'''simple docstring'''
from .. i... | 593 | 0 |
import json
import os
import unittest
from transformers import AutoTokenizer, GPTaTokenizer, GPTaTokenizerFast
from transformers.models.gpta.tokenization_gpta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tok... | 333 | """simple docstring"""
import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union
import numpy as np
def a_ ( _lowerCAmelCase : bytes , _lowerCAmelCase : int ):
'''simple docstring'''
lowercase__ : Any = f"""... | 599 | 0 |
def __lowerCamelCase ( snake_case__ ,snake_case__ ) -> str:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = [0 for i in range(r + 1 )]
# nc0 = 1
_SCREAMING_SNAKE_CASE = 1
for i in range(1 ,n + 1 ):
# to... | 569 |
import tempfile
import unittest
from make_student import create_student_by_copying_alternating_layers
from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch
UpperCamelCase = '''sshleifer/bart-ti... | 569 | 1 |
"""simple docstring"""
from collections import defaultdict
from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst
def snake_case_ ( ):
'''simple docstring'''
_lowerCamelCase , _lowerCamelCase : Optional[int] = 9, 14 # noqa: F84... | 83 |
"""simple docstring"""
from collections import deque
class lowercase:
def __init__( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) -> None:
"""simple docstring"""
a__ = process_name # process nam... | 273 | 0 |
import warnings
from ...utils import logging
from .image_processing_poolformer import PoolFormerImageProcessor
_lowerCamelCase : List[str] = logging.get_logger(__name__)
class UpperCamelCase_ ( UpperCAmelCase__ ):
'''simple docstring'''
def __init__( self : Li... | 177 |
from __future__ import annotations
import numpy as np
def SCREAMING_SNAKE_CASE ( lowercase_ ) -> List[Any]:
"""simple docstring"""
return np.maximum(0 , lowercase_ )
if __name__ == "__main__":
print(np.array(relu([-1, 0, 5]))) # --> [0, 0, 5]
| 177 | 1 |
class A :
def __init__( self : int , lowercase_ : int ) -> int:
"""simple docstring"""
_lowerCamelCase : List[Any] =n
_lowerCamelCase : List[Any] =[None] * self.n
_lowerCamelCase : str ... | 464 |
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert_fast import BertTokenizerFast
from .tokenization_dpr import DPRContextEncoderTokenize... | 529 | 0 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
from diffusers.utils imp... | 363 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image
from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcesso... | 363 | 1 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class _snake_case ( unittest.... | 284 |
'''simple docstring'''
def snake_case ( snake_case : int , snake_case : int , snake_case : int ) -> int:
"""simple docstring"""
if exponent == 1:
return base
if exponent % 2 == 0:
lowerCAmelCase = _modexpt(snake_case , exponent //... | 284 | 1 |
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class a__ ( A__ ):
A = '... | 316 | import argparse
import os
import re
import packaging.version
__lowerCamelCase : List[str] = '''examples/'''
__lowerCamelCase : List[str] = {
'''examples''': (re.compile(R'''^check_min_version\("[^"]+"\)\s*$''', re.MULTILINE), '''check_min_version("VERSION")\n'''),
''... | 316 | 1 |
'''simple docstring'''
from __future__ import annotations
import numpy as np
def _a ( _lowerCamelCase ) -> tuple[np.ndarray, np.ndarray]:
"""simple docstring"""
__snake_case , __snake_case : Optional[Any] = ... | 26 |
import time
from dataclasses import dataclass
from multiprocessing import Pool
from unittest import TestCase
from unittest.mock import patch
import multiprocess
import numpy as np
import pytest
from datasets.utils.py_utils import (
NestedDataStructure,
asdict,
iflatmap_unordered,
... | 252 | 0 |
'''simple docstring'''
import json
import os
from typing import Dict, List, Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = {
'''vocab_file''': ''... | 706 | import pytest
from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict
@pytest.mark.parametrize(
'''split_dict''' , [
SplitDict(),
SplitDict({'''train''': SplitInfo(name='''train''' , num_bytes=1_337 , num_examples=42 , dataset_name='''my_d... | 264 | 0 |
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import PoolFormerConfig, PoolFormerForImageClassification, PoolFormerImageProcessor
from transformers.utils import ... | 233 |
from urllib.parse import quote
import pytest
from datasets.utils.hub import hf_hub_url
@pytest.mark.parametrize('''repo_id''' ,['''canonical_dataset_name''', '''org-name/dataset-name'''] )
@pytest.mark.parametrize('''path''' ,['''filename.csv''', '''filename with blanks.csv'''] )
@pytest.mark.pa... | 233 | 1 |
from __future__ import annotations
_A : List[Any] = [True] * 1_000_001
_A : List[str] = 2
while i * i <= 1_000_000:
if seive[i]:
for j in range(i * i, 1_000_001, i):
_A : List[str] = False
i += 1
def __lowerCAmelCase ( snak... | 189 |
from __future__ import annotations
import unittest
from transformers import DistilBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
f... | 189 | 1 |
import numpy as np
def _lowerCAmelCase ( A__: np.ndarray , A__: float ):
'''simple docstring'''
return np.where(vector > 0 , A__ , (alpha * (np.exp(A__ ) - 1)) )
if __name__ == "__main__":
import doctest
doctest.testmod()... | 254 |
import numpy as np
def _lowerCAmelCase ( A__: np.array ):
'''simple docstring'''
return 1 / (1 + np.exp(-vector ))
if __name__ == "__main__":
import doctest
doctest.testmod()
| 254 | 1 |
"""simple docstring"""
from __future__ import annotations
def UpperCamelCase_ ( SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ ): # noqa: E741
while r - l > 1:
SCREAMING_SNAKE_CASE = (l + r) // 2
if v[m] >= k... | 406 |
"""simple docstring"""
import argparse
import collections
import os
import re
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_table.py
snake_case = 'sr... | 406 | 1 |
'''simple docstring'''
import unittest
import numpy as np
from datasets import load_dataset
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, pr... | 620 |
'''simple docstring'''
import builtins
import sys
from ...utils.imports import _is_package_available
from . import cursor, input
from .helpers import Direction, clear_line, forceWrite, linebreak, move_cursor, reset_cursor, writeColor
from .keymap import KEYMAP
UpperCamelCase__ = False
try:
UpperCamelC... | 620 | 1 |
def _A ( lowerCAmelCase_ : int ):
"""simple docstring"""
if not all(char in "01" for char in bin_string ):
raise ValueError("Non-binary value was passed to the function" )
if not bin_string:
raise ValueError("Empty string was passed to the f... | 707 |
from typing import List, Optional, Union
import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, log... | 125 | 0 |
"""simple docstring"""
import tempfile
import unittest
from make_student import create_student_by_copying_alternating_layers
from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch
lowercase_ : List[Any] ... | 572 |
"""simple docstring"""
def a__ ( snake_case__ = 10_00 ) -> int:
lowerCamelCase , lowerCamelCase = 1, 1
lowerCamelCase = 2
while True:
lowerCamelCase = 0
lowerCamelCase = fa + fa
lowerCamelCase , lowerCamelCase = fa, f
... | 543 | 0 |
# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile
from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version import Version
... | 391 |
import logging
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
BERT_START_DOCSTRING,
... | 391 | 1 |
from __future__ import annotations
import unittest
from transformers import DistilBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
fro... | 84 | '''simple docstring'''
import os
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_doctest_list.py
UpperCamelCase_ = '''.'''
if __name__ == "__main__":
UpperCamelCase_ = os.path.join(REPO_PATH, '''utils/documentation_t... | 209 | 0 |
import math
def _a ( lowerCamelCase__ ) -> bool:
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 for... | 144 |
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, ChunkPipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_t... | 144 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__snake_case ={
'configuration_biogpt': ['BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BioGptConfig'],
'tokenization_b... | 133 |
from typing import List, Union
import numpy as np
from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, logging
from .base import PIPELINE_INIT_ARGS, ArgumentHandler, ChunkPipeline
__UpperCamelCase : Optional[Any] = logging.get_logger(__name__)
... | 519 | 0 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_for... | 721 |
from __future__ import annotations
def UpperCamelCase ( lowerCAmelCase__ ):
'''simple docstring'''
if len(lowerCAmelCase__ ) < 2:
raise ValueError('''Monogons and Digons are not polygons in the Euclidean space''' )
if any(i <= 0 for i in nums ):
raise ValueError('''All ... | 633 | 0 |
_SCREAMING_SNAKE_CASE = """
# Transformers 설치 방법
! pip install transformers datasets
# 마지막 릴리스 대신 소스에서 설치하려면, 위 명령을 주석으로 바꾸고 아래 명령을 해제하세요.
# ! pip install git+https://github.com/huggingface/transformers.git
"""
_SCREAMING_SNAKE_CASE = [{"""type""": """code""", """content""": INSTALL_CONTENT}]
_SCREAMING_... | 537 | import unittest
from diffusers import FlaxAutoencoderKL
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax
from .test_modeling_common_flax import FlaxModelTesterMixin
if is_flax_available():
import jax
@require_flax
class SCREAMING_SNAKE_CASE_ ( __lowerCA... | 537 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase__ = logging.get_logger(__name__)
lowerCamelCase__ = {
'''s-JoL/Open-Llama-V1''': '''https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json''',
}
class _UpperCAmelCase ( lowerCAmelCas... | 82 | 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, ChunkPipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available(... | 82 | 1 |
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from transformers import (
VisualBertConfig,
VisualBertForMultipleChoice,
VisualBertForPreTraining,
VisualBertForQuestionAnswering,
VisualBertForVisualReasoning,
)
from transformers.utils import ... | 544 | def lowerCamelCase ( UpperCamelCase : int = 60_08_51_47_51_43 ) -> int:
try:
_lowerCamelCase = int(UpperCamelCase )
except (TypeError, ValueError):
raise TypeError('Parameter n must be int or castable to int.' )
if n <= 0:
raise ValueError('Parameter n... | 544 | 1 |
"""simple docstring"""
import argparse
import os
import re
import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCr... | 702 |
"""simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConformerConfig,
WavaVecaConformerForCTC,
WavaVecaConformerForPreTraining,
WavaVecaCTCTokenizer,
WavaVecaF... | 14 | 0 |
"""simple docstring"""
import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List
from ..utils import logging
lowerCamelCase = logging.get_logger(__name__)
def a__ ( lowerCAmelCase__=None , l... | 82 |
from __future__ import annotations
from functools import lru_cache
from math import ceil
__magic_name__: Tuple = 100
__magic_name__: Any = set(range(3, NUM_PRIMES, 2))
primes.add(2)
__magic_name__: int
for prime in range(3, ceil(NUM_PRIMES**0.5), 2):
if prime not in primes:
contin... | 324 | 0 |
'''simple docstring'''
import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.p... | 716 |
'''simple docstring'''
from __future__ import annotations
from typing import Any
class __lowercase :
'''simple docstring'''
def __init__(self ,_lowerCamelCase ) -> None:
'''simple docstring'''
__lowerca... | 56 | 0 |
"""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 __a ( A , A=() , A=None , A="no" , A="29500" ) -> List[Any]:
'''simple docs... | 337 |
"""simple docstring"""
import logging
import os
import quant_trainer
import torch
from torch.utils.data import DataLoader
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput
__UpperCAmelCase =logging.getLogger(__name__)
if is_torch_tpu_avai... | 337 | 1 |
import argparse
import datetime
def SCREAMING_SNAKE_CASE ( snake_case_ : str ):
snake_case__ : str = {
"0": "Sunday",
"1": "Monday",
"2": "Tuesday",
"3": "Wednesday",
"4": "Thursday",
"5": "Friday",
"6": "Saturday",
}
snake_cas... | 25 |
import faiss # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import requests # noqa: F401 # Here to have a nice missing dependency error message early on
import sklearn # noqa: F401 # Here t... | 25 | 1 |
from __future__ import annotations
def A__ ( _a : int ):
'''simple docstring'''
snake_case__ : str =str(_a )
return len(_a ) == 9 and set(_a ) == set("""123456789""" )
def A__ ( ):
'''simple docstring'''
... | 385 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
__lowerCamelCase : Union[str, Any] = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyN... | 385 | 1 |
import importlib
import os
import fsspec
import pytest
from fsspec import register_implementation
from fsspec.registry import _registry as _fsspec_registry
from datasets.filesystems import COMPRESSION_FILESYSTEMS, HfFileSystem, extract_path_from_uri, is_remote_filesystem
from .utils import require_lza, require_zst... | 711 |
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,
AutoTokenizer,
Data... | 268 | 0 |
import gc
import unittest
from transformers import CTRLConfig, 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_modeling_common import ModelTes... | 100 |
'''simple docstring'''
import inspect
import unittest
from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configura... | 347 | 0 |
import fire
from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer
def UpperCamelCase( lowercase_ , lowercase_ , **lowercase_ ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ = AutoConfig.from_pretrained(lowercase_ , **lowe... | 702 |
from typing import Any
def UpperCamelCase( lowercase_ ) -> list[Any]:
'''simple docstring'''
if not input_list:
return []
snake_case_ = [input_list.count(lowercase_ ) for value in input_list]
snake_case_ = max(lowercase_ ) # Gets the maximum count ... | 161 | 0 |
'''simple docstring'''
import json
from typing import Dict, List, Optional, Tuple, Union
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import Pa... | 75 |
from __future__ import annotations
def snake_case__ ( UpperCAmelCase : str ):
return [ord(UpperCAmelCase ) - 9_6 for elem in plain]
def snake_case__ ( UpperCAmelCase : list[int] ):
return "".join(chr(elem + 9_6 ) for elem in encoded )
def ... | 145 | 0 |
"""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 timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from tr... | 134 |
"""simple docstring"""
from torch import nn
class a ( nn.Module ):
def __init__( self , _lowerCamelCase , _lowerCamelCase ):
super().__init__()
lowercase = class_size
lowercase = embed_size
# self.mlp1 = nn.Linear(embed_size, emb... | 134 | 1 |
from __future__ import annotations
def _a ( lowercase__ : list ):
'''simple docstring'''
if not nums:
raise ValueError('List is empty' )
return sum(lowercase__ ) / len(lowercase__ )
if __name__ == "__main__":
import doctest
doctest.testmod(... | 85 | """simple docstring"""
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device
__SCREAMING_SNAKE_CASE =False
class UpperCamel... | 425 | 0 |
"""simple docstring"""
from __future__ import annotations
from functools import lru_cache
from math import ceil
a : int = 100
a : str = set(range(3, NUM_PRIMES, 2))
primes.add(2)
a : int
for prime in range(3, ceil(NUM_PRIMES**0.5), 2):
if prime not in pri... | 31 |
"""simple docstring"""
import argparse
import os
from io import BytesIO
from pathlib import Path
import requests
from clip_retrieval.clip_client import ClipClient
from PIL import Image
from tqdm import tqdm
def _SCREAMING_SNAKE_CASE ( _lowercase : Optional[Any] ,... | 31 | 1 |
import itertools
import json
import os
import unittest
from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_commo... | 220 |
from math import factorial, radians
def _lowerCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = 18 , SCREAMING_SNAKE_CASE = 10 ):
'''simple docstring'''
A_ = angle_in_degrees - ((angle_in_degrees // 360.0) * 360.0)
# Converting from degrees t... | 203 | 0 |
import warnings
from typing import List
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available
class UpperCAmelCase_ ( __lowercase ):
"""simpl... | 578 |
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_available():
import to... | 578 | 1 |
def a__ ( A__, A__ ):
print('\nThe shortest path matrix using Floyd Warshall algorithm\n' )
for i in range(A__ ):
for j in range(A__ ):
if dist[i][j] != float('inf' ):
print(int(dist[i][j] ), end='\t' )
else:
pr... | 101 |
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import (
is_pt_flax_cross_test,
require_flax,
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import is_flax_available, is_torch_available, is_... | 101 | 1 |
class UpperCamelCase__ : # Public class to implement a graph
"""simple docstring"""
def __init__( self , _A , _A , _A ) -> None:
SCREAMING_SNAKE_CASE_ = row
SCREAMING_SNAKE_CASE_ = col
SCREAMING_SNAKE_CASE_ = graph
... | 710 |
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartaaTokenizer, MBartaaTokenizerFast, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
... | 597 | 0 |
from __future__ import annotations
def __UpperCAmelCase ( __a : list ,__a : int ,__a : int ,__a : int ) -> list:
"""simple docstring"""
_a : List[str] = []
_a , _a : int = input_list[low:mid], input... | 14 |
'''simple docstring'''
from __future__ import annotations
import time
from math import sqrt
# 1 for manhattan, 0 for euclidean
_lowerCAmelCase : int = 0
_lowerCAmelCase : Optional[Any] = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obs... | 261 | 0 |
import functools
def __lowercase( UpperCAmelCase__ , UpperCAmelCase__ ):
"""simple docstring"""
if not isinstance(UpperCAmelCase__ , UpperCAmelCase__ ) or not all(isinstance(UpperCAmelCase__ , UpperCAmelCase__ ) for day in days ):
raise ValueError("The par... | 484 |
from tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch
from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER, require_tf, require_torch
... | 484 | 1 |
'''simple docstring'''
import os
from typing import BinaryIO, Optional, Union
import numpy as np
import pyarrow.parquet as pq
from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
from ..packaged_modules imp... | 384 |
'''simple docstring'''
from __future__ import annotations
import unittest
import numpy as np
from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf... | 384 | 1 |
"""simple docstring"""
def UpperCAmelCase__ ( lowerCAmelCase__ :int , lowerCAmelCase__ :int ) -> int:
'''simple docstring'''
if number < 0 or shift_amount < 0:
raise ValueError("""both inputs must be positive integers""" )
lowercase = str(bin... | 717 | """simple docstring"""
from __future__ import annotations
import math
def UpperCAmelCase__ ( lowerCAmelCase__ :int ) -> list[int]:
'''simple docstring'''
if num <= 0:
lowercase = f'{num}: Invalid input, please enter a positive integer.... | 197 | 0 |
import re
import string
from collections import Counter
import sacrebleu
import sacremoses
from packaging import version
import datasets
UpperCAmelCase_ : List[Any] = "\n@inproceedings{xu-etal-2016-optimizing,\n title = {Optimizing Statistical Machine Translation for Text Simp... | 21 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
snake_case__ : Union[str, Any] = {
'configuration_encodec': [
'ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP',
'EncodecConfig',
],
'feature_extrac... | 408 | 0 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by app... | 678 |
def _SCREAMING_SNAKE_CASE ( snake_case_ : list[int] , snake_case_ : list[int] ):
__magic_name__ = len(snake_case_ )
print('''The following activities are selected:''' )
# The first activity is always selected
__magic_name__ = 0
print(snake_case_ , end=''... | 678 | 1 |
import argparse
import collections
import os
import re
import tempfile
import pandas as pd
from datasets import Dataset
from huggingface_hub import hf_hub_download, upload_folder
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script... | 39 |
import bza
import gzip
import lzma
import os
import shutil
import struct
import tarfile
import warnings
import zipfile
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Dict, List, Optional, Type, Union
from .. import config
from .filelock import FileLock
from .logging ... | 39 | 1 |
'''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 accelerate.... | 11 |
'''simple docstring'''
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel
from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import H... | 11 | 1 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
... | 355 |
"""simple docstring"""
# Logistic Regression from scratch
# In[62]:
# In[63]:
# importing all the required libraries
import numpy as np
from matplotlib import pyplot as plt
from sklearn import datasets
def A_ ( snake_case__ ) -> str:
return 1 / (1 + np.exp(-z ))
def A_ ... | 355 | 1 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
from multiprocessing import get_context
from pathlib import Path
import datasets
import numpy as np
from datasets import load_dataset
from parameterized import parameterized
from transformers imp... | 56 |
'''simple docstring'''
import numpy as np
# Importing the Keras libraries and packages
import tensorflow as tf
from tensorflow.keras import layers, models
if __name__ == "__main__":
# Initialising the CNN
# (Sequential- Building the model layer by layer)
_SCREAMING_SNAKE_CASE = m... | 56 | 1 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
from multiprocessing import get_context
from pathlib import Path
import datasets
import numpy as np
from datasets import load_dataset
from parameterized import parameterized
from transformers import AutoProcessor
from ... | 139 |
import unittest
from typing import Tuple
import torch
from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch
@require_torch
class lowerCAmelCase__ :
'''simple docstring'''
@property
def _lowe... | 503 | 0 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase : List[str] = logging.get_logger(__name__)
_lowerCamelCase : Dict = {
'asapp/sew-d-tiny-100k': 'https://huggingface.co/asapp/sew-d-tiny-10... | 407 |
def _lowerCAmelCase ( __magic_name__ :Union[str, Any] , __magic_name__ :int , __magic_name__ :str ):
if n == 0:
return 1
elif n % 2 == 1:
return (binary_exponentiation(__magic_name__ , n - 1 , __magic_name__ ) * a) % mod
... | 407 | 1 |
import json
import os
import shutil
import warnings
from argparse import ArgumentParser, Namespace
from pathlib import Path
from typing import List
from ..utils import logging
from . import BaseTransformersCLICommand
try:
from cookiecutter.main import cookiecutter
a_ : Any = True
exc... | 73 |
'''simple docstring'''
import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey
class _a :
'''simple docstring'''
def __init__( self ,__a ,__a ,__a ) -> Tuple:
if dst_width < 0 or... | 116 | 0 |
import collections
import json
import os
import re
from typing import TYPE_CHECKING, List, Optional, Tuple
import numpy as np
from ...tokenization_utils_fast import PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
... | 647 | from __future__ import annotations
import math
def __lowerCamelCase (UpperCAmelCase__ : int ):
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, al... | 647 | 1 |
"""simple docstring"""
import argparse
import collections
import json
from pathlib import Path
import requests
import torch
import yaml
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileViTImageProcessor,
MobileViTVaConfig,
MobileViTVaForImageClassi... | 624 |
def A ( __UpperCamelCase , __UpperCamelCase ) -> Optional[int]:
A__ = 0
A__ = len(__UpperCamelCase ) - 1
while left <= right:
# avoid divided by 0 during interpolation
if sorted_collection[left] == sorted_collection[right]:
if sorted_collec... | 9 | 0 |
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 VaeImageProcessor
from diffusers.pip... | 709 |
import torch
from diffusers import CMStochasticIterativeScheduler
from .test_schedulers import SchedulerCommonTest
class _lowercase (a_ ):
'''simple docstring'''
lowercase__ = (CMStochasticIterativeScheduler,)
lowercase__ = 10
def _lowerCamelC... | 504 | 0 |
'''simple docstring'''
import logging
from transformers.configuration_utils import PretrainedConfig
A__ : Union[str, Any] = logging.getLogger(__name__)
class UpperCAmelCase_ (_UpperCAmelCase ):
"""simple docstring"""
lowerCamelCase : Dict = 'masked_bert... | 13 |
import logging
from transformers import PretrainedConfig
_lowerCAmelCase : str = logging.getLogger(__name__)
_lowerCAmelCase : Dict = {
'''bertabs-finetuned-cnndm''': '''https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/re... | 454 | 0 |
import logging
import os
from typing import List, TextIO, Union
from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask
__UpperCAmelCase : int = logging.getLogger(__name__)
class __snake_case ( __lowerCamelCase ):
'''sim... | 155 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCAmelCase : Optional[Any] = {
"configuration_pegasus_x": ["PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP", "PegasusXConfig"],
}
try:
if not is_torch_available():
... | 155 | 1 |
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