code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
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 PriorTransf... | 121 |
import random
from .binary_exp_mod import bin_exp_mod
def lowerCamelCase__ ( a , a=10_00 ) -> Optional[int]:
if n < 2:
return False
if n % 2 == 0:
return n == 2
# this means n is odd
_A: List[Any] = n - 1
_A: Dict = 0
while d % 2 ==... | 121 | 1 |
"""simple docstring"""
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.activations import gelu_new, gelu_python, get_activation
@require_torch
clas... | 363 |
"""simple docstring"""
from __future__ import annotations
lowerCAmelCase_ = 1.6021E-19 # units = C
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , ) -> tuple[str, float]:
if (conductivity... | 302 | 0 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_rembert im... | 284 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_snake_case : List[Any] = {
'configuration_biogpt': ['BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BioGptConfig'],
'tokenization_biogpt': ['BioGptToken... | 284 | 1 |
import math
import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def A_ ( _UpperCAmelCase = 3 ):
if isinstance(_UpperCAmelCase , _UpperCAmelCase ):
raise TypeError("number of qubits must be a integer." )
... | 127 |
import doctest
from collections import deque
import numpy as np
class __lowercase :
"""simple docstring"""
def __init__( self : Union[str, Any]):
SCREAMING_SNAKE_CASE_: int = [2, 1, 2, -1]
SCREAMING_SNAKE_CASE_: Optional[Any] = [1, 2, 3, 4]
def _SCR... | 127 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__lowerCAmelCase : Optional[int] ={'configuration_swin': ['SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SwinConfig', 'SwinOnnxConfig']}
try:
if not is_torch_available():
... | 9 |
import coval # From: git+https://github.com/ns-moosavi/coval.git # noqa: F401
from coval.conll import reader, util
from coval.eval import evaluator
import datasets
A__ = datasets.logging.get_logger(__name__)
A__ = '''\
@InProceedings{moosavi2019minimum,
author = { Naf... | 230 | 0 |
import random
import unittest
import numpy as np
import torch
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionUpscalePipeline,
PNDMScheduler,
)
from diffusers.utils import floats_tensor
from... | 367 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__A ={
'''configuration_bridgetower''': [
'''BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''BridgeTowerConfig''',
'''BridgeTowerTextConfig''',
... | 47 | 0 |
import argparse
import datetime
import io
import itertools
import json
import math
import os
import platform
import re
import shlex
import subprocess
import sys
from pathlib import Path
from statistics import fmean
import pandas as pd
import torch
from tqdm import tqdm
import transformers
lowerCamelCase__ = ... | 302 |
'''simple docstring'''
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 TFModelTeste... | 349 | 0 |
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 DPRContextEncoderTokenizer, D... | 290 |
from __future__ import annotations
import unittest
from transformers import DebertaVaConfig, 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... | 290 | 1 |
'''simple docstring'''
import gc
import unittest
from diffusers import FlaxStableDiffusionInpaintPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from fla... | 297 |
import random
import unittest
import numpy as np
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionImgaImgPipeline,
PNDMScheduler,
)
from diffusers.utils import float... | 114 | 0 |
import numpy as np
import pandas as pd
from sklearn.preprocessing import Normalizer
from sklearn.svm import SVR
from statsmodels.tsa.statespace.sarimax import SARIMAX
def _lowercase ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ) -> ... | 363 |
import json
import os
import unittest
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class __A ( a , unittest.TestCase ):
__A = ... | 262 | 0 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tok... | 71 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModel,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import AutoencoderKL, DDIMSche... | 297 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCAmelCase_ : List[str] = logging.get_logger(__name__)
UpperCAmelCase_ : ... | 368 |
"""simple docstring"""
import argparse
import os
import re
import packaging.version
UpperCAmelCase_ : Any = """examples/"""
UpperCAmelCase_ : Optional[int] = {
"""examples""": (re.compile(r"""^check_min_version\(\"[^\"]+\"\)\s*$""", re.MULTILINE), """check... | 318 | 0 |
def UpperCAmelCase_ ( __lowerCAmelCase ) -> Optional[Any]:
if edge <= 0 or not isinstance(__lowerCAmelCase , __lowerCAmelCase ):
raise ValueError('''Length must be a positive.''' )
return 3 * ((25 + 10 * (5 ** (1 / 2))) ** (1 / 2)) * (edge**2)
def UpperCAmelCase_... | 156 |
def _A ( SCREAMING_SNAKE_CASE : list ):
"""simple docstring"""
if not isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
raise ValueError("Input series is not valid, valid series - [2, 4, 6]" )
if len(SCREAMING_SNAKE_CASE ... | 95 | 0 |
import importlib.metadata
import warnings
from copy import deepcopy
from packaging import version
from ..utils import logging
from .import_utils import is_accelerate_available, is_bitsandbytes_available
if is_bitsandbytes_available():
import bitsandbytes as bnb
import torch
import torch.nn as nn
... | 165 | from typing import List, Optional, TypeVar
from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _interle... | 165 | 1 |
def SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ ) -> list:
"""simple docstring"""
A__ = len(lowercase_ )
A__ = []
for i in range(len(lowercase_ ) - pat_len + 1 ):
A__ = True
for j in ra... | 14 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_albert impo... | 302 | 0 |
"""simple docstring"""
import functools
from typing import Any
def _lowerCamelCase ( _UpperCamelCase , _UpperCamelCase ):
'''simple docstring'''
if not isinstance(_UpperCamelCase , _UpperCamelCase ) or len(_UpperCamelCase ) == 0:
raise ValueError("the string should be not e... | 363 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A : Dict = logging.get_logger(__name__)
A : List[str] = {
"dist... | 259 | 0 |
import warnings
from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum
from ...processing_utils import ProcessorMixin
if is_torch_available():
import torch
class A__ ( snake_case__ ):
... | 127 |
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
_SCREAMING_SNAKE_CASE : Optional[Any] = 2_00
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst... | 127 | 1 |
from math import isqrt, loga
def UpperCAmelCase__ (UpperCamelCase_ ):
"""simple docstring"""
snake_case = [True] * max_number
for i in range(2 ,isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j in range(i**2 ,lo... | 350 |
import argparse
import copy
def UpperCAmelCase__ (UpperCamelCase_ ):
"""simple docstring"""
snake_case = {}
with open(UpperCamelCase_ ) as f:
for line in f:
if line.split()[0] not in dict_of_neighbours:
... | 213 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
A__ = {
"""configuration_funnel""": ["""FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FunnelConfig"""],
"""con... | 82 |
'''simple docstring'''
from __future__ import annotations
from random import random
from typing import Generic, TypeVar
lowerCamelCase : Union[str, Any] = TypeVar("KT")
lowerCamelCase : Dict = TypeVar("VT")
class A__ ( Generic[KT, VT] ):
def __init__( self... | 47 | 0 |
'''simple docstring'''
def __UpperCamelCase ( _UpperCAmelCase ):
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]
__UpperCAmelCase :... | 37 |
'''simple docstring'''
import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel
class SCREAMING_SNAKE_CASE__ ( snake_case__ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE = '''M-CLIP'''
def __init__( self : List[str] ,... | 37 | 1 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoFormerTok... | 290 | """simple docstring"""
import itertools
import random
import unittest
import numpy as np
from transformers import WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST, WavaVecaConfig, WavaVecaFeatureExtractor
from transformers.testing_utils import require_torch, slow
from ...test_sequence_feature_extraction_common import Seque... | 290 | 1 |
from typing import Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
from ..models.auto.m... | 363 |
import sacrebleu as scb
from packaging import version
from sacrebleu import TER
import datasets
_UpperCAmelCase = '\\n@inproceedings{snover-etal-2006-study,\n title = "A Study of Translation Edit Rate with Targeted Human Annotation",\n author = "Snover, Matthew and\n Dorr, Bonnie ... | 232 | 0 |
"""simple docstring"""
import hashlib
import unittest
from typing import Dict
import numpy as np
from transformers import (
MODEL_FOR_MASK_GENERATION_MAPPING,
TF_MODEL_FOR_MASK_GENERATION_MAPPING,
is_vision_available,
pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from trans... | 44 |
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, Optio... | 262 | 0 |
"""simple docstring"""
from ..utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_scipy_available,
is_torch_available,
is_torchsde_available,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
... | 361 |
def __SCREAMING_SNAKE_CASE ( lowerCAmelCase: int ) -> int:
if not isinstance(lowerCAmelCase , lowerCAmelCase ):
raise TypeError("Input value must be an 'int' type" )
_UpperCAmelCase : List[Any] = 0
while number:
position += 1
number ... | 189 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowerCamelCase = {
'''configuration_rag''': ['''RagConfig'''],
'''retrieval_rag''': ['''RagRetriever'''],
'''tokenization_rag''': ['''RagTokenizer'''],
}
... | 188 |
'''simple docstring'''
import numpy as np
def lowercase_ ( _lowercase ) -> np.ndarray:
'''simple docstring'''
return 1 / (1 + np.exp(-vector ))
def lowercase_ ( _lowercase ) -> np.ndarray:
'''simple docstring'''
return vector * sigmoid(_lowercase )
if __nam... | 318 | 0 |
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
... | 148 |
import os
def _A ( ):
"""simple docstring"""
a__ : Optional[int] =os.path.join(os.path.dirname(SCREAMING_SNAKE_CASE ) , "num.txt" )
with open(SCREAMING_SNAKE_CASE ) as file_hand:
return str(sum(int(SCREAMING_SNAKE_CASE ) for line in file_hand ... | 148 | 1 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask... | 165 |
"""simple docstring"""
import argparse
import collections
import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosit... | 165 | 1 |
"""simple docstring"""
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... | 86 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class _UpperCAmelCase ( metaclass=_lowerCAmelCase ):
a__ : Union[str, Any] = ["onnx"]
def __init__( self : Any , *_lowercase : Dict , **_lowercase : Any ):
requir... | 86 | 1 |
"""simple docstring"""
from __future__ import annotations
def A_ ( _lowerCAmelCase : list[int], _lowerCAmelCase : list[int], _lowerCAmelCase : list[int], _lowerCAmelCase : list[list[str]], _lowerCAmelCase : int, ):
"""simple docstring"""
_a ... | 320 |
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_format,
)... | 259 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
snake_case__ : Tuple = logging.get_logger(__name__)
snake_case__ ... | 357 | '''simple docstring'''
from typing import Any, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler
from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from transformers... | 274 | 0 |
"""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... | 61 | """simple docstring"""
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_available... | 213 | 0 |
"""simple docstring"""
def _lowerCAmelCase ( lowerCAmelCase ):
'''simple docstring'''
return [
txt[:a] + txt[a].upper() + txt[a + 1 :]
for a in range(len(lowerCAmelCase ) )
if txt[a].isalpha()
]
if __name__ == "__main__":
_... | 248 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
lowerCAmelCase_ : str = {
'''configuration_vision_encoder_decoder''': ['''VisionE... | 248 | 1 |
'''simple docstring'''
from math import factorial
_lowerCAmelCase = {str(d): factorial(d) for d in range(10)}
def _SCREAMING_SNAKE_CASE ( UpperCamelCase ):
"""simple docstring"""
return sum(DIGIT_FACTORIAL[d] for d in str(UpperCamelCase ) )
def _SCREAMING_SNAKE_CA... | 37 |
'''simple docstring'''
import argparse
import json
import torch
from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel
def _SCREAMING_SNAKE_CASE ( UpperCamelCase , UpperCamelCase=1 ):
"""simple docstring"""
if n_shave_prefix_segments >= 0:
return "... | 37 | 1 |
import numpy as np
import torch
import tqdm
from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler
class lowercase_( lowercase ):
'''simpl... | 358 |
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging
UpperCAmelCase__ = logging.get... | 26 | 0 |
"""simple docstring"""
import os
import sys
import unittest
_UpperCamelCase: int = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))
import get_test_info # noqa: E402
from get_test_info import ( ... | 255 |
"""simple docstring"""
import json
import os
import unittest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers
from ...test_tokenization_com... | 255 | 1 |
"""simple docstring"""
import argparse
from tax import checkpoints
from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM
def __a ( __lowerCamelCase, __lowerCamelCase, __lowerCamelCase ):
UpperCAmelCase_ : Tuple = AutoConfig.from_pretrained(__lowerCamelCase )
Upper... | 23 |
"""simple docstring"""
import re
from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P
# Sentinels
_a = object()
# For specifying empty leaf dict `{}`
_a = object()
def __a ( __lower... | 23 | 1 |
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless req... | 48 |
import unittest
from transformers import load_tool
from transformers.utils import is_torch_available
if is_torch_available():
import torch
from transformers.testing_utils import require_torch
from .test_tools_common import ToolTesterMixin
@require_torch
class __a ( unit... | 189 | 0 |
from unittest.mock import Mock, patch
from file_transfer.send_file import send_file
@patch("""socket.socket""" )
@patch("""builtins.open""" )
def UpperCAmelCase__ ( UpperCAmelCase__, UpperCAmelCase__ ) -> Any:
# ===== initialization =====
A_ = Mock()
A_ = conn, Moc... | 354 |
'''simple docstring'''
from functools import lru_cache
@lru_cache
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> int:
if num < 0:
raise ValueError("""Number should not be negative.""" )
return 1 if num in (0, 1) else num * factorial(num - 1 )
if __name__ == "__m... | 101 | 0 |
"""simple docstring"""
from typing import Dict
from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.testing_utils import (
TestCasePlus,
execute_subprocess_async,
get_torch_dist_unique_port,
require_torch_multi_gpu,
... | 148 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__A = {"configuration_xlnet": ["XLNET_PR... | 148 | 1 |
'''simple docstring'''
import os
from typing import Optional
import fsspec
from fsspec.archive import AbstractArchiveFileSystem
from fsspec.utils import DEFAULT_BLOCK_SIZE
class _snake_case ( a__ ):
lowerCAmelCase :Optional[int] = ''''''
lowerCAmelCase :str ... | 283 |
'''simple docstring'''
import logging
import os
from typing import Dict, List, Optional, Union
import torch
import torch.nn as nn
from accelerate.utils.imports import (
is_abit_bnb_available,
is_abit_bnb_available,
is_bnb_available,
)
from ..big_modeling import dispatch_model, i... | 283 | 1 |
"""simple docstring"""
import os
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import xopen, xsplitext
from ..table ... | 86 |
"""simple docstring"""
import numpy as np
def __lowerCAmelCase (_UpperCamelCase ):
return 1 / (1 + np.exp(-vector ))
def __lowerCAmelCase (_UpperCamelCase ):
return vector * sigmoid(_UpperCamelCase )
if __name__ == "__main__":
import doctest
doctest.testmod() | 86 | 1 |
__A = "Tobias Carryer"
from time import time
class __lowerCAmelCase :
"""simple docstring"""
def __init__( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__=int(time() ) ) ... | 360 |
from typing import List, Optional, TypeVar
from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_dat... | 348 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_SCREAMING_SNAKE_CASE : str = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE : Optional[int] = {
"funnel-transformer/small": "https://huggingface.co/funnel-transform... | 85 |
import argparse
import csv
import logging
import os
import random
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange
from transformers import (
CONFIG_NAME,
WEIGHTS_NAME,
AdamW,
... | 274 | 0 |
from __future__ import annotations
from decimal import Decimal
from numpy import array
def __A ( __lowerCamelCase ) -> list[list[float]]:
a = Decimal
# Check if the provided matrix has 2 rows and 2 columns
# since this implementation only works for 2x2 matri... | 351 |
import warnings
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__UpperCamelCase : Dict = logging.get_logger(__name__)
class __lowerCAmelCase ( __magic_name__ ):
UpperCamelCase_... | 347 | 0 |
import argparse
import os
import sys
from unittest.mock import patch
import pytorch_lightning as pl
import timeout_decorator
import torch
from distillation import SummarizationDistiller, distill_main
from finetune import SummarizationModule, main
from transformers import MarianMTModel
from transformers.file_utils i... | 248 |
import argparse
import logging
import os
import sys
import numpy as np
import onnxruntime
import torch
from bart_onnx.generation_onnx import BARTBeamSearchGenerator
from bart_onnx.reduce_onnx_size import remove_dup_initializers
import transformers
from transformers import BartForConditionalGeneration, BartTokenizer... | 248 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCAmelCase = {
"configuration_clipseg": [
"CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP",
"CLIPSegConfig",
"CLIPSegTextConfig",
... | 357 |
def lowercase__ ( __snake_case : Tuple , __snake_case : List[Any] ):
'''simple docstring'''
UpperCAmelCase_ : Any = ''
for i in table:
res += inp[i - 1]
return res
def lowercase__ ( __snake_case... | 145 | 0 |
import math
def lowerCamelCase__ ( a = 1_00 ) -> Tuple:
_A: Optional[Any] = sum(i * i for i in range(1 , n + 1 ) )
_A: Optional[Any] = int(math.pow(sum(range(1 , n + 1 ) ) , 2 ) )
return square_of_sum - sum_of_squares
if... | 121 |
def lowerCAmelCase_ ( snake_case_,snake_case_ ):
while b:
_A , _A : List[str] = b, a % b
return a
def lowerCAmelCase_ ( snake_case_,snake_case_ ):
return a if b == 0 else euclidean_gcd_recursive(snake_case_,a % b )
def ... | 26 | 0 |
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
snake_case : int = ''''''
snake_case : int = ''''''
snake_case : int = ''''''
snake_case : Any = 1 # (0 is vertical, 1 is horizontal)
def __lowerCamelCase ( ):
... | 281 |
def __lowerCamelCase ( UpperCAmelCase_ : int = 100 ):
"""simple docstring"""
a :List[Any] = 0
a :List[Any] = 0
for i in range(1 , n + 1 ):
sum_of_squares += i**2
sum_of_ints += i
return sum_of_int... | 281 | 1 |
'''simple docstring'''
import argparse
import json
import os
import pickle
import shutil
import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset
from transformers import (
BertConfig,
BertForMaskedLM,
BertTokenizer,
... | 23 |
'''simple docstring'''
import os
import tempfile
import unittest
import numpy as np
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax, slow
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_... | 23 | 1 |
import argparse
import torch
from torch import nn
from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration
def lowerCamelCase__ (_UpperCAmelCase):
SCREAMING_SNAKE_CASE = [
'encoder.version',
'decoder.version',
'model.encod... | 356 |
import pytest
from datasets import inspect_metric, list_metrics, load_metric
@pytest.fixture
def lowerCamelCase__ (_UpperCAmelCase):
monkeypatch.setattr('datasets.utils.deprecation_utils._emitted_deprecation_warnings' , set())
@pytest.fixture
def lowerCamelCase__ (_UpperCAmelCa... | 327 | 0 |
import math
def lowerCamelCase__ ( snake_case_ : int ) -> bool:
assert isinstance(snake_case_ , snake_case_ ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2 and 3 are primes
return... | 24 |
import unittest
import numpy as np
import torch
from torch import nn
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import KandinskyVaaPriorPipeline, PriorTransformer,... | 101 | 0 |
def __lowercase ( __lowerCAmelCase : Optional[Any] = 6_0_0_8_5_1_4_7_5_1_4_3 ):
try:
a__ = int(__UpperCAmelCase )
except (TypeError, ValueError):
raise TypeError('Parameter n must be int or castable to int.' )
if n <=... | 352 |
from __future__ import annotations
class snake_case_ :
def __init__( self :List[str] ,__snake_case :int ) -> None:
a__ = data
a__ = None
a__ = None
def __lowercase ( __lowerCAmelCase : ... | 109 | 0 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin,... | 283 |
import json
import os
import unittest
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class UpperCAmelCase_ ( UpperCamelCase , unittest.TestCas... | 283 | 1 |
from __future__ import annotations
import unittest
from transformers import RoFormerConfig, 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
... | 361 |
class lowerCamelCase :
"""simple docstring"""
def __init__( self : int, _UpperCAmelCase : Dict, _UpperCAmelCase : str ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CAS... | 191 | 0 |
"""simple docstring"""
def a_ ( lowerCamelCase = 1_0_0_0 ):
return sum(2 * a * ((a - 1) // 2) for a in range(3 , n + 1 ) )
if __name__ == "__main__":
print(solution())
| 98 | import os
from typing import Dict, List, Tuple, TypeVar, Union
__snake_case = TypeVar('''T''')
__snake_case = Union[List[T], Tuple[T, ...]]
__snake_case = Union[T, List[T], Dict[str, T]]
__snake_case = Union[str, bytes, os.PathLike]
| 348 | 0 |
import warnings
from ...utils import logging
from .image_processing_yolos import YolosImageProcessor
lowerCAmelCase__ : Union[str, Any] =logging.get_logger(__name__)
class UpperCAmelCase_ ( UpperCamelCase_ ):
'''simple docstring'''
def __init__( self , *_A , ... | 371 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ : Dict =logging.get_logger(__name__)
lowerCAmelCase__ : Optional[int] ={
'''asapp/sew-tiny-100k''': '''https://huggingface.co/asapp/sew-tiny-100k/resolve/m... | 118 | 0 |
'''simple docstring'''
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextModel,
CLIPTokenizer,
WhisperForConditionalGeneration,
WhisperProcessor,
)
from diffusers import (
AutoencoderKL,
DDIMScheduler,... | 267 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
UpperCAmelCase : Optional[int] = logging.get_logger(__name__)
UpperCAmelCase : str ... | 267 | 1 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bert import BertTokenizer
_UpperCAmelCase : Union[str, Any] = logging.get_logger(__name__)
_Uppe... | 359 |
import argparse
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedTyp... | 110 | 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 appl... | 92 | '''simple docstring'''
from json import JSONDecodeError # Workaround for requests.exceptions.JSONDecodeError
import requests
def __UpperCAmelCase ( a_: str = "isbn/0140328726" ):
_UpperCAmelCase : Dict = olid.strip().strip("/" ) # Remove leading/trailing whitespac... | 145 | 0 |
def snake_case( __magic_name__ ) -> Optional[int]:
'''simple docstring'''
lowercase : List[Any] = len(A__ )
for i in range(A__ ):
for j in range(i + 1 , A__ ):
if numbers[j] < numbers[i]:
lower... | 351 |
import os
from collections.abc import Iterator
def snake_case( __magic_name__ = "." ) -> Iterator[str]:
'''simple docstring'''
for dir_path, dir_names, filenames in os.walk(__magic_name__ ):
lowercase : Tuple = [d for d in dir_na... | 116 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
snake_case : Optional[Any] = {"configuration_fnet": ["FNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "FNetConfig"]}
... | 281 |
import unittest
from transformers import BigBirdTokenizer, BigBirdTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
snake_case... | 281 | 1 |
def lowerCamelCase__ ( ) -> int:
'''simple docstring'''
return 1
def lowerCamelCase__ ( UpperCamelCase__ : int ) -> int:
'''simple docstring'''
return 0 if x < 0 else two_pence(x - 2 ) + one_pence()
def lowerCamelCase__ ... | 295 |
import argparse
import OmegaConf
import torch
from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel
def lowerCamelCase__ ( UpperCamelCase__ : Dict , UpperCamelCase__ : List[str] , UpperCamelCase__ : Dict ) -> List[Any]:
'''si... | 295 | 1 |
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
__lowerCAmelCase... | 88 |
from math import pi
def SCREAMING_SNAKE_CASE__ ( __a , __a ):
return 2 * pi * radius * (angle / 3_60)
if __name__ == "__main__":
print(arc_length(90, 10))
| 327 | 0 |
"""simple docstring"""
def _UpperCamelCase ( UpperCamelCase_ : str ) -> list:
"""simple docstring"""
lowerCAmelCase__ = [0] * len(UpperCamelCase_ )
for i in range(1 , len(UpperCamelCase_ ) ):
# use last results for b... | 357 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__snake_case : Dict = {"""configuration_focalnet""": ["""FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FocalNetConfig"""]}
tr... | 122 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A: List[str] = logging.get_logger(__name__)
A: Tuple = {
"s... | 109 |
"""simple docstring"""
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfig... | 109 | 1 |
from __future__ import annotations
lowerCamelCase_ = 1.6021e-19 # units = C
def lowerCamelCase ( a_ , a_ , a_ , ) -> tuple[str, float]:
if (conductivity, electron_conc, mobility).count(0 ) != 1:
raise ValueError('You cannot... | 14 |
import warnings
from ...utils import logging
from .image_processing_poolformer import PoolFormerImageProcessor
lowerCamelCase_ = logging.get_logger(__name__)
class a_ ( a_ ):
'''simple docstring'''
def __init__( self , ... | 14 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase : Optional[Any] =logging.get_logger(__name__)
_UpperCAmelCase : Optional[int] ={
"""funnel-transformer/small""": """https://huggingface.co/funnel-transformer/small/resolve/main/config.json""",
"""f... | 262 |
"""simple docstring"""
def __lowerCamelCase ( a_ : Union[str, Any] ) -> Optional[int]:
__SCREAMING_SNAKE_CASE :List[str] = 1
__SCREAMING_SNAKE_CASE :Dict = 2
while i * i <= n:
__SCREAMING_SNAKE_CASE :Tuple = 0
... | 191 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
_UpperCAmelCase : Any = {"""configuration_fnet""": ["""FNET_PRETRAINED_CONFIG_ARCHIVE_MA... | 366 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class a__ ( metaclass=__A ):
"""simple docstring"""
__UpperCamelCase : int = ['torch', 'scipy']
def __init__(self , *__lowercase , **__lowercase ):
... | 9 | 0 |
'''simple docstring'''
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 A__ ( SCREAMING_SNAKE_CASE__ ... | 162 | from ...processing_utils import ProcessorMixin
class lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
lowerCamelCase__ = ['''image_processor''', '''feature_extractor''']
lowerCamelCase__ = '''TvltImageProcessor'''
lowerCamelCase__ = '''TvltFeatureExtractor'''
... | 118 | 0 |
'''simple docstring'''
import os
import textwrap
import pyarrow as pa
import pytest
from datasets import ClassLabel, Features, Image
from datasets.packaged_modules.csv.csv import Csv
from ..utils import require_pil
@pytest.fixture
def __A ( lowerCAmelCase_ ):
_UpperCAmelCase : ... | 170 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor
lowerCAmelCase_ : Union[str, Any] = logging.get_logger(__name__)
class __lowerCAmelCase ( __a ):
def __init__(... | 170 | 1 |
'''simple docstring'''
import flax.linen as nn
import jax
import jax.numpy as jnp
class SCREAMING_SNAKE_CASE( nn.Module ):
"""simple docstring"""
lowerCamelCase__ = 42
lowerCamelCase__ = jnp.floataa
def A ( ... | 23 |
def _a ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
"""simple docstring"""
if exponent == 1:
return base
if exponent % 2 == 0:
lowercase__ = _modexpt(SCREAMING_SNAKE_CASE , exponent // 2 , SCREAMI... | 110 | 0 |
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sag... | 308 |
from __future__ import annotations
import time
import numpy as np
_lowerCAmelCase : List[str] = [8, 5, 9, 7]
_lowerCAmelCase : List[str] = [
[2, 0, 1, 1],
[0, 1, 2, 1],
[4, 0, 0, 3],
[0, 2, 1, 0],
[1, 0, 3, 0],
]
_lowerCAmelCase : List[Any] ... | 308 | 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
lowerCamelCase__ = logging.get_logger(__name__)
lowerCamelCase__ ... | 86 |
from manim import *
class SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
def _lowerCAmelCase ( self ):
A : Union[str, Any] = Rectangle(height=0.5, width=0.5 )
A : Optional[int] = Rectangle(he... | 116 | 0 |
import argparse
import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers import (
CLIPTokenizer,
CLIPTokenizerFast,
VideoMAEImageProcessor,
XCLIPConfig,
XCLIPModel,
XCLIPProcessor,
XCLIPTextConfig,
XCLIPVisionCo... | 221 |
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class __lowerCamelCase ( UpperCamelCase__ ):
"""simple docstring"""
snake_case__ = "Speech2TextFeatureExtractor"
snake_case__ = "Speech2TextTokenizer"
... | 221 | 1 |
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import DeiTConfig
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 ... | 227 |
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis
from lavis.models import load_model_and_preprocess
from PIL import Image
from transformers import (
AutoTokeni... | 227 | 1 |
import os
def UpperCamelCase ( __lowercase : Dict ):
'''simple docstring'''
A_ : Dict = len(grid[0] )
A_ : int = len(__lowercase )
A_ : List[Any] = 0
A_ : str = 0
A_ : int = 0
... | 192 | from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version(""">=""", """4.25.0""")):
raise OptionalDependencyNotAvailable()
... | 192 | 1 |
'''simple docstring'''
import argparse
import math
import os
from copy import deepcopy
import torch
from audio_diffusion.models import DiffusionAttnUnetaD
from diffusion import sampling
from torch import nn
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
_lowerCAmelCase ... | 37 |
_A = [0, 2, 4, 6, 8]
_A = [1, 3, 5, 7, 9]
def lowerCamelCase__ ( a__ : int , a__ : int , a__ : list[int] , a__ : int ) -> int:
if remaining_length == 0:
if digits[0] == 0 or digits[-1] == 0:
return ... | 122 | 0 |
import gc
import inspect
import unittest
import torch
from parameterized import parameterized
from diffusers import PriorTransformer
from diffusers.utils import floats_tensor, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import Model... | 50 | a_ = [
'DownloadConfig',
'DownloadManager',
'DownloadMode',
'StreamingDownloadManager',
]
from .download_config import DownloadConfig
from .download_manager import DownloadManager, DownloadMode
from .streaming_download_manager import StreamingDownloadManager
| 50 | 1 |
from __future__ import annotations
_lowerCamelCase : Optional[Any] = 1.60_21E-19 # units = C
def SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ , lowercase_ , ) -> tuple[str, float]:
"""simple docstring"""
if (conductivity, electron_conc, mobilit... | 14 |
_lowerCamelCase : Optional[int] = 65521
def SCREAMING_SNAKE_CASE ( lowercase_ ) -> int:
"""simple docstring"""
A__ = 1
A__ = 0
for plain_chr in plain_text:
A__ = (a + ord(lowercase_ )) % MOD_A... | 14 | 1 |
"""simple docstring"""
from typing import List
from .keymap import KEYMAP, get_character
def _snake_case ( UpperCamelCase : str ):
def decorator(UpperCamelCase : Optional[int] ):
UpperCAmelCase : List[Any] = getattr(UpperCamelCase , """handle_key""" ,... | 76 |
"""simple docstring"""
import os
from collections.abc import Iterator
def _snake_case ( UpperCamelCase : str = "." ):
for dir_path, dir_names, filenames in os.walk(UpperCamelCase ):
UpperCAmelCase : List[Any] = [d for d in dir_names if d != """scripts""" and d[0] not in... | 76 | 1 |
"""simple docstring"""
from __future__ import annotations
import time
_a = list[tuple[int, int]]
_a = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0],
[1, 0, 1, 0, 0, 0, 0],
... | 17 |
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow
@require_torch
class _lowercase ( unittest.TestCase ):
'''simple docstring'''
def __magi... | 9 | 0 |
"""simple docstring"""
import argparse
import glob
import logging
import os
import sys
import time
from collections import defaultdict
from pathlib import Path
from typing import Dict, List, Tuple
import numpy as np
import pytorch_lightning as pl
import torch
from callbacks import SeqaSeqLoggingCallback, get_checkpo... | 368 |
"""simple docstring"""
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokeni... | 161 | 0 |
import argparse
import intel_extension_for_pytorch as ipex
import torch
from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline
_lowercase : int =argparse.ArgumentParser("Stable Diffusion script with intel optimization", add_help=False)
parser.add_argument("--dpm", acti... | 170 |
import itertools
from dataclasses import dataclass
from typing import Optional
import pandas as pd
import pyarrow as pa
import datasets
from datasets.table import table_cast
@dataclass
class snake_case__ (datasets.BuilderConfig ):
"""simple docstring"""
_... | 170 | 1 |
import os
import textwrap
import pyarrow as pa
import pytest
from datasets import ClassLabel, Features, Image
from datasets.packaged_modules.csv.csv import Csv
from ..utils import require_pil
@pytest.fixture
def snake_case__ ( lowerCamelCase__ : Optional[int] ) -> U... | 364 |
'''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
from ..auto import CONFIG_MAPPING
snake_case__ = l... | 4 | 0 |
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemak... | 308 |
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 requir... | 308 | 1 |
def _UpperCamelCase ( UpperCamelCase_ : str ) -> str:
"""simple docstring"""
return "".join(chr(ord(UpperCamelCase_ ) - 32 ) if 'a' <= char <= 'z' else char for char in word )
if __name__ == "__main__":
from doctest import testmod
testmod()
... | 366 |
def _UpperCamelCase ( UpperCamelCase_ : str ) -> str:
"""simple docstring"""
return " ".join(input_str.split()[::-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 122 | 0 |
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_CACHE,
WEIGHTS_NAME,
cached_fil... | 232 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase : List[str] = {'configuration_vit_msn': ['VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTMSNConfig']}
try:
if not is_torch_available():
raise OptionalD... | 232 | 1 |
def A_ ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
SCREAMING_SNAKE_CASE_: str = (num_of_terms / 2) * (2 * first_term + (num_of_terms - 1) * common_diff)
# formula for sum of series
return total
def A_ ( ):
print(sum_of_series(1 ... | 127 |
import os
# Precomputes a list of the 100 first triangular numbers
lowerCAmelCase : Optional[int] = [int(0.5 * n * (n + 1)) for n in range(1, 101)]
def A_ ( ):
SCREAMING_SNAKE_CASE_: List[str] = os.path.dirname(os.path.realpath(_UpperCAmelCase ) )
SCREAMI... | 127 | 1 |
import inspect
import unittest
from transformers import YolosConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_mode... | 192 |
import warnings
from ...utils import logging
from .image_processing_donut import DonutImageProcessor
A_ : Union[str, Any] = logging.get_logger(__name__)
class _a (__magic_name__ ):
'''simple docstring'''
def __init__( self , *A__ , **A__ ):
... | 192 | 1 |
"""simple docstring"""
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase : Any , __UpperCAmelCase : List[str] , __UpperCAmelCase : str ):
if exponent == 1:
return base
if exponent % 2 == 0:
_lowercase : int = _modexpt(__UpperCAmelCase , expon... | 354 |
"""simple docstring"""
from __future__ import annotations
from typing import TypedDict
class UpperCamelCase ( snake_case ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : str
SCREAMING_SNAKE_CASE_ : int
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase... | 336 | 0 |
import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel
class lowerCAmelCase ( __UpperCamelCase ):
UpperCAmelCase__ = """M-CLIP"""
def __init__( self : Optional[Any] , UpperCAmelCase : Union[str, Any]=1024 , UpperCAmelCas... | 50 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase , _UpperCAmelCase ) -> list[tuple[int, int]]:
lowerCamelCase__ , lowerCamelCase__ : Union[str, Any] = position
lowerCamelCase__ : Optional[Any] = [
(y + 1,... | 50 | 1 |
'''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_modelin... | 362 |
'''simple docstring'''
from typing import List, Optional, Tuple, Union
import torch
from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
lowerCAmelCase__ = logging.get_logger(__name__) # pylint: disable=invalid-name
class lower... | 52 | 0 |
from collections import defaultdict
from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst
def lowerCamelCase__ ( ):
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : Union[str, Any] = 9, 14 # noqa: F841
SCREAMING_SNAKE_CASE : List[str] = [
[0, 1, 4... | 76 |
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_res... | 76 | 1 |
"""simple docstring"""
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, PerceiverTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...t... | 359 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCamelCase: str = logging.get_logger(__name__)
_UpperCamelCase: Union[str, Any] = {
'alibaba-damo/mgp-str-base': 'https://huggingface.co/alibaba... | 53 | 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_modelin... | 250 |
'''simple docstring'''
from __future__ import annotations
def snake_case ( UpperCAmelCase )-> list[int]:
"""simple docstring"""
__A = 2
__A = []
while i * i <= n:
if n % i:
i += 1
els... | 161 | 0 |
'''simple docstring'''
def _lowerCamelCase ( lowerCamelCase_ : Optional[Any] ):
"""simple docstring"""
if a < 0:
raise ValueError('Input value must be a positive integer' )
elif isinstance(_lowerCAmelCase , _lowerCAmelCase ):
... | 354 | '''simple docstring'''
from typing import Optional, Union
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin
class __SCREAMING_SNAKE_CASE ( lowerCamelCase_ , lowerCamelCase_ ):
'''simp... | 274 | 0 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transfor... | 103 |
'''simple docstring'''
def a_ ( lowerCamelCase : Optional[Any] ):
return [
{
0: [1, 2],
1: [0, 2],
2: [0, 1, 3, 5],
3: [2, 4],
4: [3],
5: [2, 6, 8],
6: [5, 7],
7: [6, 8],
8:... | 4 | 0 |
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, ByTaTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...test_tokenization_common import TokenizerTesterMixin
i... | 47 |
import copy
import re
class _SCREAMING_SNAKE_CASE :
lowerCAmelCase__ = 'hp'
lowerCAmelCase__ = {}
lowerCAmelCase__ = None
@classmethod
def SCREAMING_SNAKE_CASE_( cls , lowercase , lowercase ) -> Tuple:
lowerCamelCase_ = prefix
... | 47 | 1 |
import math
lowerCAmelCase__ : Dict = 10
lowerCAmelCase__ : Dict = 7
lowerCAmelCase__ : Optional[Any] = BALLS_PER_COLOUR * NUM_COLOURS
def UpperCamelCase__ ( A__ = 20 ) -> str:
snake_case__ : Union[str, Any] = math.comb(a__ , a__ )
sn... | 143 |
import unittest
from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
@require_sentencepiece
@slow # see ht... | 122 | 0 |
import re
def SCREAMING_SNAKE_CASE ( __UpperCamelCase) -> bool:
a = re.compile(
r"^(?:0|94|\+94|0{2}94)" r"7(0|1|2|4|5|6|7|8)" r"(-| |)" r"\d{7}$")
return bool(re.search(__UpperCamelCase , __UpperCamelCase))
if __name__ == "__main__":
lowercase__ : Un... | 180 |
import math
def SCREAMING_SNAKE_CASE ( __UpperCamelCase) -> 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
... | 180 | 1 |
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