code stringlengths 82 53.2k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
'''simple docstring'''
def __snake_case ( _UpperCAmelCase : str):
return credit_card_number.startswith(('''34''', '''35''', '''37''', '''4''', '''5''', '''6'''))
def __snake_case ( _UpperCAmelCase : str):
UpperCamelCase = credit_card_number
UpperCamelC... | 212 |
'''simple docstring'''
def __snake_case ( _UpperCAmelCase : int):
UpperCamelCase = n ** (1 / 3)
return (val * val * val) == n
if __name__ == "__main__":
print(perfect_cube(27))
print(perfect_cube(4))
| 212 | 1 |
'''simple docstring'''
from __future__ import annotations
def UpperCamelCase__ ( _lowercase : dict , _lowercase : str ) -> str:
__UpperCAmelCase, __UpperCAmelCase: Dict = set(snake_case__ ), [start]
while stack:
__UpperCAmelCase: int = stack.pop()
... | 700 | '''simple docstring'''
import argparse
import os
import torch
from transformers.utils import WEIGHTS_NAME
SCREAMING_SNAKE_CASE_ = ['small', 'medium', 'large']
SCREAMING_SNAKE_CASE_ = 'lm_head.decoder.weight'
SCREAMING_SNAKE_CASE_ = 'lm_head.weight'
def UpperCamelCase__ ( _lowerc... | 466 | 0 |
from __future__ import annotations
from bisect import bisect_left
from functools import total_ordering
from heapq import merge
@total_ordering
class UpperCamelCase__ ( _UpperCAmelCase ):
def __lt__(self : Union[str, Any] , snake_case_ : int ):
return self[-1] < other[-1... | 521 | import numpy as np
def __lowerCAmelCase ( _A ,_A ,_A = 1E-12 ,_A = 100 ,):
"""simple docstring"""
assert np.shape(_A )[0] == np.shape(_A )[1]
# Ensure proper dimensionality.
assert np.shape(_A )[0] == np.shape(_A )[0]
# Ensure inputs ... | 398 | 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 convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
OPENAI_CLIP_MEAN,
OPENA... | 108 |
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_to... | 108 | 1 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import tensorflow as tf
from transformers import AutoTokenizer,... | 169 |
"""simple docstring"""
import torch
from diffusers import DDIMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class SCREAMING_SNAKE_CASE__ ( _a ):
_a = (DDIMParallelScheduler,)
_a = (('eta', 0.0), ('num_inference_steps', 50))
... | 169 | 1 |
"""simple docstring"""
import warnings
from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline # noqa F401
warnings.warn(
"The `inpainting.py` script is outdated. Please use directly `from diffusers import"
" StableDiffusionInpaintPipeline` instead."
)
| 595 | """simple docstring"""
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import TimesformerConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from t... | 595 | 1 |
"""simple docstring"""
def snake_case_ ( A_ : int = 1_00_00_00 ):
'''simple docstring'''
_lowerCamelCase : str = set(range(3, A_, 2 ) )
primes.add(2 )
for p in range(3, A_, 2 ):
if p not in primes:... | 83 |
'''simple docstring'''
# Copyright 2022 The HuggingFace Team and The OpenBMB 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.o... | 685 | 0 |
'''simple docstring'''
import os
from distutils.util import strtobool
def lowercase_ ( lowercase__ , lowercase__ ) ->Optional[Any]:
for e in env_keys:
_snake_case: List[str] = int(os.environ.get(lowercase__ , -1 ) )
if val >= 0... | 721 |
'''simple docstring'''
from sklearn.metrics import matthews_corrcoef
import datasets
A : Dict = '\nCompute the Matthews correlation coefficient (MCC)\n\nThe Matthews correlation coefficient is used in machine learning as a\nmeasure of the quality of binary and multiclass classifications. ... | 273 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
UpperCamelCase__ = {
'''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/config.json''',
''... | 75 |
"""simple docstring"""
def a__ ( ) -> list[list[int]]:
return [list(range(1_0_0_0 - i , -1_0_0_0 - i , -1 ) ) for i in range(1_0_0_0 )]
__A = generate_large_matrix()
__A = (
[[4, 3, 2, -1], [3, 2, 1, -1], [1, 1, -1, -2], [-1, -1, ... | 346 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
A__ : Optional[int] = {
"configuration_rag": ["RagConfig"],
"retrieval_rag": ["RagRetriever"],
"tokenization_rag": ["Ra... | 718 |
'''simple docstring'''
import os
import zipfile
import pytest
from datasets.utils.extract import (
BzipaExtractor,
Extractor,
GzipExtractor,
LzaExtractor,
SevenZipExtractor,
TarExtractor,
XzExtractor,
ZipExtractor,
ZstdExtractor,
)
from .utils import require_lza, require_... | 124 | 0 |
import math
from typing import Any, Callable, List, Optional, Tuple, Union
import numpy as np
import torch
from ...models import TaFilmDecoder
from ...schedulers import DDPMScheduler
from ...utils import is_onnx_available, logging, randn_tensor
if is_onnx_available():
from ..onnx_utils import OnnxRu... | 345 |
import unittest
from transformers import TrOCRConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tens... | 35 | 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_vid... | 716 | """simple docstring"""
from datetime import datetime
import matplotlib.pyplot as plt
import torch
def snake_case__ ( _snake_case : Dict ):
"""simple docstring"""
for param in module.parameters():
UpperCamelCase__ = False
def snake_case__ ( ):... | 304 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any
@dataclass
class a :
"""simple docstring"""
__lowerCAmelCase = 42
__lowerCAmelCase = None... | 523 | '''simple docstring'''
import argparse
import json
from tqdm import tqdm
def UpperCamelCase__ ( ) -> Optional[Any]:
__UpperCAmelCase: Dict = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--src_path""" , type=_lowercase , default... | 523 | 1 |
"""simple docstring"""
def lowerCamelCase__ ( __snake_case=2_81_23 ) -> Optional[int]:
"""simple docstring"""
_UpperCamelCase = [1] * (limit + 1)
for i in range(2, int(limit**0.5 ) + 1 ):
sum_divs[i * i] += i
for k in... | 78 |
"""simple docstring"""
from typing import Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make... | 78 | 1 |
import json
import os
from typing import Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
'vocab_file': 'vocab.json',
'me... | 406 |
import argparse
import torch
from transformers import YosoConfig, YosoForMaskedLM
def lowercase__ ( __snake_case : Optional[Any] ):
'''simple docstring'''
if "model" in orig_key:
UpperCAmelCase_ : Optional[int] = orig_key.replace('model.... | 406 | 1 |
"""simple docstring"""
from __future__ import annotations
def A_ ( __lowercase ):
UpperCamelCase_ : List[Any] =0.00
UpperCamelCase_ : Dict =0
for resistor in resistors:
if resistor <= 0:
UpperCamelCase_ : List[str] =F'''Resistor at index {index} ha... | 395 |
"""simple docstring"""
def A_ ( __lowercase ):
UpperCamelCase_ : List[str] =''
for ch in key:
if ch == " " or ch not in key_no_dups and ch.isalpha():
key_no_dups += ch
return key_no_dups
def A_ ( __lowercase ):
UpperCamelCase_ : int =[chr(i + 65 ... | 395 | 1 |
from typing import Dict
import numpy as np
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline, PipelineException
if is_tf_available():
import tensorflow as tf
from ..tf_utils import ... | 79 |
'''simple docstring'''
import argparse
import os
import jax as jnp
import numpy as onp
import torch
import torch.nn as nn
from music_spectrogram_diffusion import inference
from tax import checkpoints
from diffusers import DDPMScheduler, OnnxRuntimeModel, SpectrogramDiffusionPipeline
from diffusers.pipelines.s... | 495 | 0 |
import random
def a_ ( lowerCAmelCase_ : Union[str, Any], lowerCAmelCase_ : Union[str, Any], lowerCAmelCase_ : Union[str, Any] ):
__lowerCAmelCase = a[left_index]
__lowerCAmelCase = left_index + 1
for j in range(left_index + 1, lowerCAmelCase_ ... | 705 |
import argparse
import random
import joblib
import numpy as np
import torch
from igf.igf import (
SecondaryLearner,
collect_objective_set,
compute_perplexity,
generate_datasets,
load_gpta,
recopy_gpta,
set_seed,
train_secondary_learner,
)
from torch.utils.data import DataLoader, R... | 421 | 0 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_distilbert import DistilBertTokenizer
A__ : List[str] = logging.get_logger(... | 13 |
'''simple docstring'''
from typing import Dict
import numpy as np
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline, PipelineException
if is_tf_available():
import tensorflow as tf
from ..tf_utils import... | 664 | 0 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
snake_case__ = {
'''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/config.json''',
'''albert-large-v1''': '''https://huggingfa... | 719 |
import sys
from collections import defaultdict
class lowerCAmelCase_ :
def __init__( self : Optional[int] ) ->Any:
"""simple docstring"""
a__ :Optional[Any] = []
def _snake_case ( self : Optional[Any] , __A : ... | 373 | 0 |
'''simple docstring'''
def __snake_case ( SCREAMING_SNAKE_CASE_ : str ) -> Optional[int]:
"""simple docstring"""
return "".join(chr(ord(snake_case__ ) - 32 ) if '''a''' <= char <= '''z''' else char for char in word )
if __name__ == "__main__":
from doctest import testmo... | 51 |
'''simple docstring'''
def _A ( snake_case__ : list[int] , snake_case__ : list[int] ):
snake_case__ : Tuple = len(snake_case__ )
print('''The following activities are selected:''' )
# The first activity is always selected
snake_case__ : Optional[Any] ... | 261 | 0 |
import torch
from ..models.speechta import SpeechTaForTextToSpeech, SpeechTaHifiGan, SpeechTaProcessor
from ..utils import is_datasets_available
from .base import PipelineTool
if is_datasets_available():
from datasets import load_dataset
class a ( __lowerCAmelCase ):
... | 704 | def snake_case ( snake_case__ :int = 1_000_000) -> int:
_A = set(range(3 , snake_case__ , 2))
primes.add(2)
for p in range(3 , snake_case__ , 2):
if p not in primes:
continue
primes.difference... | 83 | 0 |
"""simple docstring"""
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import flax
import jax.numpy as jnp
from jax import random
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedule... | 554 |
from __future__ import annotations
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import ConfigTester... | 302 | 0 |
'''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 flax.j... | 162 |
'''simple docstring'''
import json
import os
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common ... | 162 | 1 |
'''simple docstring'''
import re
from pathlib import Path
from unittest import TestCase
import pytest
@pytest.mark.integration
class a__ ( a_ ):
'''simple docstring'''
def lowerCAmelCase ( self : Optional[int] , lowerCAmelCase_ : str ) -> Any:
... | 186 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import is_tf_available, is_torch_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, SMALL_MODEL_IDENTIFIER, is_pt_tf_cross_test, slow
if is_tf_available():
from transformers import (
... | 186 | 1 |
'''simple docstring'''
import collections
import os
import re
from pathlib import Path
__UpperCAmelCase = 'src/transformers'
# Matches is_xxx_available()
__UpperCAmelCase = re.compile(r'is\_([a-z_]*)_available()')
# Catches a one-line _import_struct = {xxx}
__UpperCAmelCase = re.c... | 717 |
'''simple docstring'''
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Value
from .base import TaskTemplate
@dataclass(frozen=SCREAMING_SNAKE_CASE )
class _a ( SCREAMING_SNAKE_CASE ):
... | 220 | 0 |
import inspect
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
import torch.utils.checkpoint
from ...models import UNetaDModel, VQModel
from ...schedulers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
E... | 268 |
from PIL import Image
def lowerCamelCase__ ( __A :Image ):
"""simple docstring"""
__snake_case , __snake_case = image.size
__snake_case = 0
__snake_case = image.load()
for i in range(__A ):
... | 268 | 1 |
"""simple docstring"""
from __future__ import annotations
import typing
from collections.abc import Iterable
import numpy as np
lowercase__ : int = typing.Union[Iterable[float], Iterable[int], np.ndarray] # noqa: UP007
lowercase__ : Optional[Any] = typing.Union[np.fl... | 485 |
"""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 Te... | 485 | 1 |
'''simple docstring'''
import json
import os
import unittest
from transformers.models.roc_bert.tokenization_roc_bert import (
VOCAB_FILES_NAMES,
RoCBertBasicTokenizer,
RoCBertTokenizer,
RoCBertWordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.testing_ut... | 384 |
'''simple docstring'''
import warnings
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_... | 384 | 1 |
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
if is_flax_available():
import... | 704 |
from typing import List
from .keymap import KEYMAP, get_character
def _A ( _UpperCamelCase ):
def decorator(_UpperCamelCase ):
_UpperCAmelCase : Optional[int] = getattr(_UpperCamelCase , '''handle_key''' , [] )
handle += [key]
setattr(_UpperCamelCase ,... | 416 | 0 |
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import TimesformerConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transfo... | 217 |
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_gpta import GPTaTokenizer
... | 217 | 1 |
'''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class a_ ( _snake_case ):
lowercase = ['image_processor', 'tokenizer']
lowercase = 'CLIPImageProcessor'
lowercase = ('CLIPT... | 713 |
'''simple docstring'''
from math import factorial
def lowercase__ ( __UpperCamelCase = 20 )-> int:
UpperCamelCase = 2 * n # middle entry of odd rows starting at row 3 is the solution for n = 1,
# 2, 3,...
UpperCamelCase ... | 35 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCAmelCase : Union[str, Any] = {
'''configuration_table_transformer''': [
'''TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
... | 46 |
"""simple docstring"""
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class __lowe... | 594 | 0 |
from __future__ import annotations
import math
def __a ( __UpperCAmelCase : int ) -> Dict:
"""simple docstring"""
if num <= 0:
lowerCamelCase_ : List[Any] = f"{num}: Invalid input, please enter a positive integer."
raise ValueE... | 705 |
# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that... | 253 | 0 |
import gc
import unittest
import numpy as np
import torch
from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import ena... | 235 |
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,
require_torch_neuroncore... | 235 | 1 |
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, ... | 713 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_snake_case = {
"configuration_time_series_transformer": [
"TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"TimeSeriesTransformerConfig",
],
}
try:
... | 54 | 0 |
'''simple docstring'''
import logging
import os
import threading
import time
try:
import warnings
except ImportError:
lowercase : Optional[Any] = None
try:
import msvcrt
except ImportError:
lowercase : Union[str, Any] = None
try:
... | 649 |
'''simple docstring'''
def __a ( A__ = 1000 ) -> int:
lowerCAmelCase = 3
lowerCAmelCase = 0
while a < n:
if a % 3 == 0 or a % 5 == 0:
result += a
elif a % 15 == 0:
result -= a
a += 1
return result
if __name__ == "__main__... | 649 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowercase_ = {
"configuration_mobilebert": [
"MOBILEBERT_PRETRAINED_CONFIG_ARCHIV... | 715 | def __lowerCAmelCase ( __SCREAMING_SNAKE_CASE : List[str] ):
'''simple docstring'''
__snake_case : int = 1
__snake_case : Any = 2
while i * i <= n:
__snake_case : Tuple = 0
while n % i == 0:
n... | 390 | 0 |
"""simple docstring"""
from scipy.stats import pearsonr
import datasets
a_ = '''
Pearson correlation coefficient and p-value for testing non-correlation.
The Pearson correlation coefficient measures the linear relationship between two datasets. The calculation of the p-value relies on the assumption... | 480 |
"""simple docstring"""
from typing import Optional
from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
class __lowercase ( _UpperCAmelCase):
"""simple docst... | 480 | 1 |
"""simple docstring"""
import unittest
from parameterized import parameterized
from transformers import AutoTokenizer, GPTNeoXConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTester... | 31 |
"""simple docstring"""
import itertools
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union
import pandas as pd
import pyarrow as pa
import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datase... | 31 | 1 |
import unittest
import numpy as np
from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
nightly,
require_onnxruntime,
require_torch_gpu,
)
from ..test_pipelines_onnx_common import O... | 12 |
'''simple docstring'''
from typing import Optional, Union
import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention
from ...modeli... | 585 | 0 |
"""simple docstring"""
import logging
import math
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union
import torch
from .tensor_utils import tensor_tree_map, tree_map
def UpperCamelCase ( UpperCAmelCase ) ->List[Tuple[int, ...]]:
... | 712 |
"""simple docstring"""
def UpperCamelCase ( UpperCAmelCase ) ->bool:
"""simple docstring"""
a_ = 0
for ch in input_str:
a_ = ord(UpperCAmelCase )
a_ = pow(2 , UpperCAmelCase )
# If we already turned on bit for current character's... | 210 | 0 |
import random
import unittest
import torch
from diffusers import IFImgaImgSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params impo... | 563 |
from __future__ import annotations
from math import pow, sqrt
def _A ( SCREAMING_SNAKE_CASE : float , SCREAMING_SNAKE_CASE : float , SCREAMING_SNAKE_CASE : float ):
"""simple docstring"""
if (resistance, reactance, impedance).count(0 ) != 1:
raise ValueErr... | 563 | 1 |
import copy
import inspect
import unittest
from transformers import AutoBackbone
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import require_timm, require_torch, torch_device
from transformers.utils.import_utils import is_torch_available
from ...test_bac... | 643 |
import io
import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__UpperCAmelCase : Any = logging.get_logger(__name__)
__UpperCAmelCase : int = "▁"... | 643 | 1 |
import cmath
import math
def UpperCAmelCase__ ( __magic_name__ : float , __magic_name__ : float , __magic_name__ : float , __magic_name__ : float ):
'''simple docstring'''
lowerCAmelCase : Tuple = math.radians(_A )
lowerCAmelCase : int ... | 348 |
import argparse
import json
import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers import (
VideoMAEConfig,
VideoMAEForPreTraining,
VideoMAEForVideoClassification,
VideoMAEImageProcessor,
)
def UpperCamelCase__ ( _A: Tuple ):
... | 479 | 0 |
import warnings
from typing import Dict
import numpy as np
from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQUENCE_CLASSIFICATI... | 682 |
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applic... | 682 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
"facebook/dpr-ctx_encoder-single-nq-base": (
"https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-ba... | 41 |
'''simple docstring'''
import baseaa
import io
import json
import os
from copy import deepcopy
from ..optimizer import AcceleratedOptimizer
from ..scheduler import AcceleratedScheduler
class _snake_case :
'''simple docstring'''
def ... | 436 | 0 |
from __future__ import annotations
import math
from collections import Counter
from string import ascii_lowercase
def __UpperCamelCase ( _A ):
lowerCAmelCase_ , lowerCAmelCase_ = analyze_text(_A )
lowerCAmelCase_ = list(''' ''' + ascii_lowercase )
... | 710 |
def __UpperCamelCase ( _A ):
lowerCAmelCase_ = [int(_A ) for i in ip_va_address.split('''.''' ) if i.isdigit()]
return len(_A ) == 4 and all(0 <= int(_A ) <= 254 for octet in octets )
if __name__ == "__main__":
_A = input().strip()
... | 325 | 0 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer
UpperCAmelCase__ : int = logging.get_logger(__name__)
... | 410 | from statistics import mean, stdev
def A ( _lowercase , _lowercase = 3 ):
SCREAMING_SNAKE_CASE : int = min(_lowercase )
SCREAMING_SNAKE_CASE : Optional[Any] = max(_lowercase )
# normalize data
return [round((x - x_min) / (... | 248 | 0 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless re... | 441 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifie... | 441 | 1 |
from collections import deque
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase ) -> Union[str, Any]:
snake_case__ = len(__lowerCAmelCase )
snake_case__ = deque()
snake_case__ = [False for _ in range(__lowerCAmelCase )]
snake_case__ = [-1 for _ in range(__... | 33 |
import os
import tempfile
import unittest
from transformers import FlaubertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tenso... | 568 | 0 |
'''simple docstring'''
import enum
import os
from hashlib import shaaaa
from typing import Optional
from .. import config
from .logging import get_logger
a : Optional[int] = get_logger(__name__)
class SCREAMING_SNAKE_CASE__ ( enum.Enum ):
__SCREAMING_SNAKE_CASE ... | 680 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a : Union[str, Any] = logging.get_logger(__name__)
a : List[Any] = {
... | 680 | 1 |
"""simple docstring"""
def lowercase__(A ) ->list:
"""simple docstring"""
if len(A ) <= 1:
return lst
lowercase__ : Any= 1
while i < len(A ):
if lst[i - 1] <= lst[i]:
i += 1
... | 218 |
"""simple docstring"""
from __future__ import annotations
from decimal import Decimal
from numpy import array
def lowercase__(A ) ->list[list[float]]:
"""simple docstring"""
lowercase__ : str= Decimal
# Check if the prov... | 218 | 1 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad
class A ( unittest.TestCase ):
'''simple docstring'''
def a_ (self ) ... | 399 |
'''simple docstring'''
def __lowerCAmelCase ( snake_case__ ):
return [
txt[:a] + txt[a].upper() + txt[a + 1 :]
for a in range(len(snake_case__ ) )
if txt[a].isalpha()
]
if __name__ == "__main__":
__import__('''doctest''').testmod()
| 399 | 1 |
'''simple docstring'''
def __lowerCAmelCase ( a_ ) -> Any:
'''simple docstring'''
SCREAMING_SNAKE_CASE : str = set()
# To detect a back edge, keep track of vertices currently in the recursion stack
SCREAMING_SNAKE_CA... | 251 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_SCREAMING_SNAKE_CASE : List[Any] = {
"configuration_funnel": ["FUNNEL_PRETRAINED... | 400 | 0 |
"""simple docstring"""
def UpperCAmelCase__ ( SCREAMING_SNAKE_CASE : Dict , SCREAMING_SNAKE_CASE : Any ):
'''simple docstring'''
lowerCAmelCase = word.split()
def justify(SCREAMING_SNAKE_CASE : Optional[Any] , SCREAMING_SNAKE_CASE : ... | 718 |
"""simple docstring"""
import os
import sys
import unittest
SCREAMING_SNAKE_CASE__ = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))
import check_dummies # noqa: E402
from check_dummies import create_dummy_files, ... | 393 | 0 |
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 = {
'''bert-base-uncased''': '''https://huggingface.co/b... | 40 |
import math
import random
def UpperCamelCase ( snake_case__ : float , snake_case__ : bool = False ) -> float:
if deriv:
return value * (1 - value)
return 1 / (1 + math.exp(-value ))
# Initial Value
__UpperCAmelCase = 0.02
def UpperCamelCase ... | 40 | 1 |
import cmath
import math
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =math.radians(SCREAMING_SNAKE_CASE_ )
SCREAMING_SNAKE_CASE =math.radians(SCREAMING_SNA... | 703 |
import unittest
import numpy as np
from transformers import DistilBertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from transf... | 252 | 0 |
"""simple docstring"""
import json
import re
from typing import TYPE_CHECKING, List, Optional, Tuple, Union
import numpy as np
from ...utils import is_tf_available, is_torch_available, logging
if TYPE_CHECKING:
if is_torch_available():
import torch
if is_tf_available():
imp... | 516 |
'''simple docstring'''
import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor
def __lowerCamelCase ( SCREAMING_SNAK... | 421 | 0 |
import os
from argparse import ArgumentParser, Namespace
from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand
if not is_tf_available() an... | 613 |
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by app... | 613 | 1 |
"""simple docstring"""
import re
import string
import numpy as np
import datasets
a__ : Dict = """
Returns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.
"""
a__ : List[str] ... | 589 |
"""simple docstring"""
from string import ascii_uppercase
a__ : Any = {char: i for i, char in enumerate(ascii_uppercase)}
a__ : str = dict(enumerate(ascii_uppercase))
def A__ ( __lowerCamelCase, __lowerCamelCase ):
"""simple docstring"... | 589 | 1 |
"""simple docstring"""
import argparse
import json
from tqdm import tqdm
def lowerCamelCase_( ) -> Any:
'''simple docstring'''
_lowerCamelCase : Dict = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--src_path" , type=_l... | 386 |
"""simple docstring"""
import random
import unittest
import torch
from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import... | 386 | 1 |
'''simple docstring'''
import unittest
import torch
from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMixin, UNetTesterMixin
enable_full_determinism()
class ... | 209 |
"""simple docstring"""
from typing import Optional, Tuple, Union
import tensorflow as tf
from ...activations_tf import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_tf_outputs import (
TFBaseModelOutputWithNoAtte... | 153 | 0 |
import unittest
import numpy as np
import requests
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_av... | 495 |
def UpperCamelCase ( lowercase_ = 10_00 ) -> int:
'''simple docstring'''
return sum(2 * a * ((a - 1) // 2) for a in range(3 , n + 1 ) )
if __name__ == "__main__":
print(solution())
| 495 | 1 |
'''simple docstring'''
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,... | 8 |
'''simple docstring'''
from __future__ import annotations
def _lowerCAmelCase ( __snake_case : list[int] , __snake_case : list[int] , __snake_case : int ) -> tuple[float, list[float]]:
__A : int = list(range(len(__snake... | 8 | 1 |
import os
# Precomputes a list of the 100 first triangular numbers
__lowerCamelCase = [int(0.5 * n * (n + 1)) for n in range(1, 101)]
def UpperCamelCase__ ( ) -> List[Any]:
"""simple docstring"""
_a : Optional[Any] = os.path.dirname(os.path.realpa... | 307 |
from typing import Optional
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import (
BackboneOutput,
BaseModelOutputWithNoAttention,
BaseModelOutputWi... | 307 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowerCamelCase__ = {
"""configuration_mvp""": ["""MVP_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MvpConfig""", """MvpOnnxConfig"""],
"""tokenization_mvp""": ["""... | 225 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
lowerCamelCase__ = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
... | 225 | 1 |
'''simple docstring'''
import operator
def snake_case ( snake_case : list , snake_case : bool = False , snake_case : list | None = None ) -> list:
"""simple docstring"""
lowerCAmelCase = operator.lt if reverse else operator.gt
lowerCAmelCase = s... | 719 |
'''simple docstring'''
import torch
def snake_case ( ) -> List[str]:
"""simple docstring"""
if torch.cuda.is_available():
lowerCAmelCase = torch.cuda.device_count()
else:
lowerCAmelCase = 0
print(F'Successfully ran on {num_gpus} GPUs' )
if __... | 514 | 0 |
"""simple docstring"""
import argparse
import torch
from transformers import (
SpeechTaConfig,
SpeechTaFeatureExtractor,
SpeechTaForSpeechToSpeech,
SpeechTaForSpeechToText,
SpeechTaForTextToSpeech,
SpeechTaProcessor,
SpeechTaTokenizer,
logging,
)
from transformers.tokenizat... | 642 |
"""simple docstring"""
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 = logging.get_logger(__name__)
clas... | 642 | 1 |
'''simple docstring'''
from __future__ import annotations
def UpperCamelCase ( lowercase_ : str , lowercase_ : List[str] , lowercase_ : Tuple , lowercase_ : int ) -> List[str]:
'''simple docstring'''
lowercase =[]
lowercase , lowerca... | 714 |
'''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 UpperCamelCase ( lowercase_ : List[str] , lowercase_ : Optional[Any] , ... | 145 | 0 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class __UpperCamelCase ( metaclass=lowerCAmelCase_ ):
A_ = ["transformers", "torch", "note_seq"]
def __init__( self , *__a , **__a ):
'''simple docstring'''
req... | 476 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel
from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_... | 476 | 1 |
'''simple docstring'''
from .imports import is_tqdm_available
if is_tqdm_available():
from tqdm.auto import tqdm as _tqdm
from ..state import PartialState
def a__ ( lowerCAmelCase__ = True , *lowerCAmelCase__ , **lowerCAmelCase__ ) -> Union[str, Any]:
... | 312 |
'''simple docstring'''
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
... | 312 | 1 |
import importlib
import inspect
import json
import os
import re
import shutil
import sys
from pathlib import Path
from typing import Dict, Optional, Union
from urllib import request
from huggingface_hub import HfFolder, cached_download, hf_hub_download, model_info
from packaging import version
... | 253 |
import tempfile
import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel
from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from di... | 253 | 1 |
"""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
i... | 491 |
"""simple docstring"""
def __snake_case ( SCREAMING_SNAKE_CASE: str ):
"""simple docstring"""
_lowerCAmelCase = 0
# if input_string is "aba" than new_input_string become "a|b|a"
_lowerCAmelCase = ''
_lowerCAmelCase = ''
... | 491 | 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
_snake_case : Optional[int] = logging.get_logger(__na... | 22 |
'''simple docstring'''
from collections.abc import Generator
from math import sin
def snake_case_ (UpperCamelCase : bytes ):
'''simple docstring'''
if len(UpperCamelCase ) != 32:
raise ValueError('''Input must be of length 32''' )
... | 22 | 1 |
"""simple docstring"""
from collections.abc import Sequence
from queue import Queue
class a_ :
def __init__( self : List[Any] , snake_case__ : Any , snake_case__ : Optional[int] , snake_case__ : Optional[int] , snake_case__ : ... | 674 | """simple docstring"""
import copy
import os
import cva
import numpy as np
from matplotlib import pyplot as plt
class a_ :
def __init__( self : Optional[int] ):
lowerCAmelCase__ = """"""
lowerCAmelCase__ = """"""
lowerCAmelCase__ = [... | 674 | 1 |
import multiprocessing
from typing import TYPE_CHECKING, Optional, Union
from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream
if TYPE_CHECKING:
import sqlitea
import ... | 100 |
"""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
f... | 589 | 0 |
import os
from glob import glob
import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn
from transformers import CLIPModel, CLIPTokenizerFast
f... | 402 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__a: Optional[Any] = logging.get_logger(__name__)
__a: str = {
'''google/bit-50''': '''https://... | 402 | 1 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandi... | 78 |
import datasets
import faiss
import numpy as np
import streamlit as st
import torch
from elasticsearch import Elasticsearch
from elia_utils import (
embed_questions_for_retrieval,
make_qa_sas_model,
qa_sas_generate,
query_es_index,
query_qa_dense_index,
)
import transformers
from transformer... | 297 | 0 |
from collections import OrderedDict
from typing import Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...toke... | 700 |
# limitations under the License.
from typing import Optional, Tuple, Union
import torch
from diffusers import DiffusionPipeline, ImagePipelineOutput
class A (SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__( self : Optional[Any] , __... | 247 | 0 |
"""simple docstring"""
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _lowerCAmelCase ( SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
_lowerCamelCase = ['''image_processor''', '''to... | 58 |
from typing import Optional, Tuple, Union
import tensorflow as tf
from ...activations_tf import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_tf_outputs import (
TFBaseModelOutputWithNoAttention,
TFBaseMode... | 652 | 0 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaInpaintPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_... | 514 |
'''simple docstring'''
def snake_case ( snake_case : int ) -> Tuple:
"""simple docstring"""
lowerCAmelCase = 0
lowerCAmelCase = len(snake_case )
for i in range(n - 1 ):
for j in range(i + 1 , snake_case ):
if arr[i] > arr[j]:
... | 514 | 1 |
def lowerCamelCase__ ( _lowercase , _lowercase = " " ):
'''simple docstring'''
UpperCAmelCase_ : int = []
UpperCAmelCase_ : List[str] = 0
for index, char in enumerate(_UpperCamelCase ):
if char == separator:
split_words.append(strin... | 30 |
"""simple docstring"""
from argparse import ArgumentParser
from .env import EnvironmentCommand
def lowerCamelCase ( ) -> Optional[int]:
'''simple docstring'''
__UpperCAmelCase : Any = ArgumentParser("""Diffusers CLI tool""" , usage="""diffusers-cli <command> [<args>]"... | 139 | 0 |
from __future__ import annotations
import typing
from collections.abc import Iterable
import numpy as np
lowercase__ : Union[str, Any] = typing.Union[Iterable[float], Iterable[int], np.ndarray] # noqa: UP007
lowercase__ : Optional[Any] = typing.Union[np.floataa, int, float]... | 139 |
import unittest
from transformers import LiltConfig, 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 ModelTe... | 139 | 1 |
from __future__ import annotations
def lowercase ( _lowerCAmelCase ):
# preprocessing the first row
for i in range(1 , len(matrix[0] ) ):
matrix[0][i] += matrix[0][i - 1]
# preprocessing the first column
for i in range(1 , len(_lowerCAmelCase ) ):
matrix[i][0] ... | 392 |
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.models import Sequential
if __name__ == "__main__":
snake_case__ : Tuple = pd.read_csv('''sample_data.csv''', header=None)
... | 392 | 1 |
_lowerCAmelCase : Union[str, Any] = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"
def UpperCAmelCase_ ( snake_case__ ) -> bytes:
"""simple docstring"""
if not isinstance(snake_case__ , snake_case__ ):
lowerCAmelCase__ ... | 604 |
from __future__ import annotations
import os
import tempfile
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import is_tensorflow_text_available, is_tf_available
from transformers.testing_utils import require_tensorflow_text, require_tf, slow
from ..test_modeling... | 604 | 1 |
"""simple docstring"""
from __future__ import annotations
class a :
def __init__( self : Optional[int] , __lowerCAmelCase : list[list[int]] ):
_UpperCAmelCase = TypeError(
"""Matrices must be formed from a list of zero or more lists containing at ""... | 277 | """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 HeunDi... | 277 | 1 |
_SCREAMING_SNAKE_CASE : Any = {
'''Pillow''': '''Pillow<10.0.0''',
'''accelerate''': '''accelerate>=0.20.3''',
'''av''': '''av==9.2.0''',
'''beautifulsoup4''': '''beautifulsoup4''',
'''black''': '''black~=23.1''',
'''codecarbon''': '''codecarbon==1.2.0''',
'''cookiecutter'... | 472 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusionPanoramaPipeline,
UNe... | 472 | 1 |
import json
import os
import unittest
from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class lowercase_ ... | 20 |
from collections import Counter
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
snake_case = datasets.load_iris()
snake_case = np.array(data["""data"""])
snake_case = np.array(data["""target"""])
snake_case = data["""... | 62 | 0 |
"""simple docstring"""
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDe... | 701 |
"""simple docstring"""
from __future__ import annotations
from collections import Counter
from random import random
class snake_case__ :
def __init__( self : Any ):
'''simple docstring'''
UpperCAmelCase : List[Any] = {}
def __lowerCA... | 292 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase_ = {
'configuration_xlm_roberta_xl': [
'XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP',
'XLMRobertaXLConfig... | 560 |
"""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,
... | 560 | 1 |
'''simple docstring'''
import tempfile
import unittest
from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
torch_device,
)
from ...generation.test_utils import GenerationTesterMixin
fr... | 39 |
'''simple docstring'''
import os
import sys
import unittest
lowerCAmelCase : str = 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 ( # noqa: E402
get_m... | 39 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_lowerCAmelCase :int = logging.get_logger(__name__)
_lowerCAmelCase :Optional[Any] ... | 251 |
import json
import os
from typing import Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__magic_name__ = logging.get_logger(__name__)
__magic_name__ = {
'''vocab_file''': '''vocab.json''',
'''merges_file''': '''merges.t... | 276 | 0 |
'''simple docstring'''
def _A ( _lowerCAmelCase = 1_000 ):
"""simple docstring"""
return sum(2 * a * ((a - 1) // 2) for a in range(3 , n + 1 ) )
if __name__ == "__main__":
print(solution())
| 454 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import _LazyModule
lowerCamelCase = {"""tokenization_bertweet""": ["""BertweetTokenizer"""]}
if TYPE_CHECKING:
from .tokenization_bertweet import BertweetTokenizer
else:
import sys
lowerCamelCase = _LazyModule(__name... | 454 | 1 |
from ....configuration_utils import PretrainedConfig
from ....utils import logging
UpperCamelCase__ : Any = logging.get_logger(__name__)
UpperCamelCase__ : Any = {
'''Visual-Attention-Network/van-base''': (
'''https://huggingface.co/Visual-Attention-Network/van-base/blob/ma... | 105 |
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 import (
Fla... | 105 | 1 |
from __future__ import annotations
import unittest
from transformers import LEDConfig, 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
from ...test_pipeline_mixin... | 488 |
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by ap... | 488 | 1 |
"""simple docstring"""
def SCREAMING_SNAKE_CASE ( _lowerCamelCase : int | float | str ) -> tuple[int, int]:
try:
_lowerCAmelCase : List[str] = float(_lowerCamelCase )
except ValueError:
raise ValueError("""Please enter a valid number""" )
_lowerCAmelCase : int... | 213 | """simple docstring"""
# Copyright (c) 2021-, NVIDIA CORPORATION. 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
... | 213 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCAmelCase = logging.get_logger(__name__)
__lowerCAmelCase = {
"""edbeeching/decision-transformer-gym-hopper-medium""": (
"""https://huggingface.co/edbeeching/decision-transformer-gym-hopper-medium/re... | 589 |
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_tokeni... | 589 | 1 |
'''simple docstring'''
def _a( UpperCamelCase__ : list, UpperCamelCase__ : list, UpperCamelCase__ : int ):
'''simple docstring'''
if len(UpperCamelCase__ ) != len(UpperCamelCase__ ):
raise ValueError('''The length o... | 296 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_ima... | 296 | 1 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from tr... | 672 |
'''simple docstring'''
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
a : List[str] = logging.get_logger(__name__)... | 672 | 1 |
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