code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
'''simple docstring'''
def SCREAMING_SNAKE_CASE( __lowercase ) -> str: # noqa: E741
A: str = len(__lowercase )
A: Optional[Any] = 0
A: Any = [0] * n
A: List[Any] = [False] * n
A: List[s... | 319 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE( __lowercase = 1 , __lowercase = 1_0_0_0 ) -> int:
A: Any = 1
A: Optional[Any] = 0
for divide_by_number in range(__lowercase , digit + 1 ):
A: li... | 319 | 1 |
"""simple docstring"""
from functools import reduce
SCREAMING_SNAKE_CASE_ : List[str] = (
'73167176531330624919225119674426574742355349194934'
'96983520312774506326239578318016984801869478851843'
'85861560789112949495459501737958331952853208805511'
'125406987471585238630... | 69 |
"""simple docstring"""
def _snake_case ( UpperCAmelCase_ : int = 50 ):
A__ = [1] * (length + 1)
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_start in range(row_length - tile_length +... | 69 | 1 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_funnel import FunnelTokenizer
lowercase : int = logging.get_l... | 3 |
"""simple docstring"""
import random
import unittest
import numpy as np
import torch
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionUpscalePipeline,
PNDMScheduler,
)
from diffusers.uti... | 203 | 0 |
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingface import HuggingF... | 358 |
import logging
import os
from .state import PartialState
class lowercase ( logging.LoggerAdapter ):
@staticmethod
def __UpperCamelCase ( A_ ) -> Optional[Any]:
"""simple docstring"""
UpperCamelCase = PartialState()
return not main_process_only or (main_process_only an... | 110 | 0 |
"""simple docstring"""
import logging
import torch
from accelerate import Accelerator
from arguments import EvaluationArguments
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from transformers import AutoModelFo... | 64 |
"""simple docstring"""
from .integrations import (
is_optuna_available,
is_ray_available,
is_sigopt_available,
is_wandb_available,
run_hp_search_optuna,
run_hp_search_ray,
run_hp_search_sigopt,
run_hp_search_wandb,
)
from .trainer_utils import (
HPSearchBack... | 64 | 1 |
from math import pow
def lowerCAmelCase_ ( __a , __a , __a , __a , __a , ) -> tuple[int, int]:
"""simple docstring"""
if current_sum == needed_sum:
# If the sum of the powers is equal to needed_sum, then we have a solution.
solutions_count += ... | 273 |
from __future__ import annotations
from typing import Any
class _SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__(self : Tuple , UpperCAmelCase_ : int , UpperCAmelCase_ : int , UpperCAmelCase_ : float = 0) ->None:
'''simple docstr... | 273 | 1 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__snake_case =logging.get_logger(__name__)
__snake_ca... | 4 |
'''simple docstring'''
def lowerCamelCase__ ( _A ):
return 10 - x * x
def lowerCamelCase__ ( _A , _A ):
# Bolzano theory in order to find if there is a root between a and b
if equation(_A ) * equation(_A ) >= 0:
raise ValueError('Wrong space!' )
... | 297 | 0 |
'''simple docstring'''
from typing import Dict, List, Optional, Type
from .. import config
from ..utils import logging
from .formatting import (
ArrowFormatter,
CustomFormatter,
Formatter,
PandasFormatter,
PythonFormatter,
TensorFormatter,
format_table,
query_table,
)
from .np_forma... | 350 |
'''simple docstring'''
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import doctest
import sys
import warnings
from os.path import abspath, dirname, join
import _pytest
from transformers.testing_utils import HfDoctestModule, HfDocTestParser
# allow ha... | 275 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
_a = {
'configuration_wav2vec2': ['WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Wav2Vec2Con... | 61 | """simple docstring"""
_a : List[str] = {
'Pillow': 'Pillow',
'accelerate': 'accelerate>=0.11.0',
'compel': 'compel==0.1.8',
'black': 'black~=23.1',
'datasets': 'datasets',
'filelock': 'filelock',
'flax': 'flax>=0.4.1',
'hf-doc-builder': 'hf-doc-builder>=0.3.0',
... | 44 | 0 |
"""simple docstring"""
from unittest import TestCase
from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset
class __lowerCamelCase ( A__ ):
'''simple docstring'''
def lowerCamelCase ( self : List[Any] ):
return [
{"col... | 161 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ = logging.get_logger(__name__)
lowercase__ = {
"""SCUT-DLVCLab/lilt-roberta-en-base""": (
"""https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/main/... | 161 | 1 |
"""simple docstring"""
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union
# Build the list of all image processors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_re... | 132 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_owlvit import OwlViTImageProcessor
a :Optional[Any] = logging.get_logger(__name__)
class __a (UpperCamelCase_):
'''simple docstring'''
def __init__( self , *_a , **_a ) ... | 132 | 1 |
'''simple docstring'''
import numpy as np
import torch
from torch.utils.data import Dataset
from utils import logger
class a__ ( UpperCAmelCase__ ):
def __init__( self : str , a : Optional[int] , a : List[Any] ):
"""simple docstring"""
__lowe... | 237 | '''simple docstring'''
import time
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device
from ..test_modeling_common import ids_tensor
if is_torch_available():
import torch
from transformers.generation import (
MaxLengthCriteria... | 237 | 1 |
"""simple docstring"""
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_d... | 69 | """simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_... | 150 | 0 |
"""simple docstring"""
import qiskit
def __SCREAMING_SNAKE_CASE ( lowercase__ , lowercase__ ):
"""simple docstring"""
A = qiskit.Aer.get_backend("aer_simulator" )
# Create a Quantum Circuit acting on the q register
A = qiskit.QuantumCir... | 364 |
"""simple docstring"""
from __future__ import annotations
class __UpperCamelCase :
def __init__(self : Tuple , __SCREAMING_SNAKE_CASE : int = 0):
A = key
def SCREAMING_SNAKE_CASE__ (self : str , __SCREAMING_SNAKE_CASE : str , __SCREAMING_... | 57 | 0 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mobilebert import MobileBertTokenizer
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ ... | 5 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase : List[str] = logging.get_logger(__name__)
UpperCAmelCase : Union[str, Any] = {
"EleutherAI/gpt-neox-20b": "https://huggingface.co/EleutherAI/gpt-neox-20b... | 136 | 0 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
lowerCamelCase_ : Any = {"""tokenization_byt5""": ["""ByT5Tokenizer"""]}
if TYPE_CHECKING:
from .tokenization_byta import ByTaTokenizer
else:
import sys
lowerCamelCase_ : Any = _LazyModule(__name__, globals()["""... | 197 | from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase_ : Tuple = logging.get_logger(__name__)
lowerCamelCase_ : List[Any] = {
"""google/fnet-base""": """https://huggingface.co/google/fnet-base/resolve/main/config.json""",
"""google/fnet-la... | 197 | 1 |
"""simple docstring"""
import bza
import gzip
import lzma
import os
import shutil
import struct
import tarfile
import warnings
import zipfile
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Dict, List, Optional, Type, Union
from .. import config
from .filelock import FileLock
from ... | 77 |
'''simple docstring'''
from __future__ import annotations
import requests
_A : str =set(
'''approved_at_utc approved_by author_flair_background_color
author_flair_css_class author_flair_richtext author_flair_template_id author_fullname
author_premium can_mod_post catego... | 41 | 0 |
'''simple docstring'''
import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging
if is_torch_avail... | 275 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_beit import BeitImageProcessor
lowerCAmelCase :Tuple = logging.get_logger(__name__)
class _lowerCamelCase ( lowercase__ ):
'''simple docstring'''
def __init__( self : Any , ... | 275 | 1 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import RoFormerConfig, 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... | 294 |
"""simple docstring"""
A : int = {
"Pillow": "Pillow",
"accelerate": "accelerate>=0.11.0",
"compel": "compel==0.1.8",
"black": "black~=23.1",
"datasets": "datasets",
"filelock": "filelock",
"flax": "flax>=0.4.1",
"hf-doc-builder": "hf-doc-builder>=0.3.0",
"huggingf... | 57 | 0 |
import unittest
from transformers import (
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TextaTextGenerationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils import... | 26 |
import math
def _a ( a :int = 100 ) -> int:
a = sum(i * i for i in range(1 , n + 1 ) )
a = int(math.pow(sum(range(1 , n + 1 ) ) , 2 ) )
return square_of_sum - sum_of_squares
if __name__ == "__main__":
print(f... | 26 | 1 |
"""simple docstring"""
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 Tokenizer... | 197 | """simple docstring"""
from __future__ import annotations
from collections.abc import Callable
__lowerCAmelCase : str =list[list[float | int]]
def UpperCAmelCase__ ( lowerCAmelCase__ :Matrix , lowerCAmelCase__ :Matrix ) -> Matrix:
'''simple d... | 197 | 1 |
'''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,
... | 237 | '''simple docstring'''
import unittest
from transformers import DebertaVaTokenizer, DebertaVaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
__UpperCAmelCase =get_tests_dir... | 237 | 1 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
... | 208 |
'''simple docstring'''
import inspect
import warnings
from typing import Any, Dict, Optional, Union
from packaging import version
def a_ ( *_lowerCAmelCase ,_lowerCAmelCase = None ,_lowerCAmelCase=True ,_lowerCAmelCase=2 ) -> List[str]:
fr... | 208 | 1 |
'''simple docstring'''
import argparse
import os
import re
import packaging.version
_SCREAMING_SNAKE_CASE = '''examples/'''
_SCREAMING_SNAKE_CASE = {
'''examples''': (re.compile(R'''^check_min_version\("[^"]+"\)\s*$''', re.MULTILINE), '''check_min_version("VERSION")\n'''... | 368 |
'''simple docstring'''
def _lowerCAmelCase ( lowerCamelCase_ : Union[str, Any] ):
__lowercase = 1
__lowercase = 2
while i * i <= n:
__lowercase = 0
while n % i == 0:
n //= i
multiplicity += 1
n_divisor... | 217 | 0 |
'''simple docstring'''
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
if is_torch_available():
import torch
from transformers import XLMRobertaModel
@require_se... | 152 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING, TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
from transformers.pipelines import AudioClassificationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_t... | 152 | 1 |
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import evaluate
import numpy as np
import torch
from datasets import load_dataset
from PIL import Image
from torchvision.transforms import (
CenterCrop,
Compose,
Normalize,
RandomHorizontalFlip,
... | 304 |
# 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 applic... | 304 | 1 |
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
from .record_evaluation import evaluate as evaluate_record
__UpperCAmelCase = '\\n@article{wang2019superglue,\n title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},\n aut... | 29 | import warnings
from typing import List
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available
class lowercase_ ( UpperCamelCase_ ):
"""simple d... | 338 | 0 |
"""simple docstring"""
import unittest
from typing import Tuple
import torch
from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch
@require_torch
class UpperCamelCase_ :
@property
def _SCREAMING_SN... | 351 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import _LazyModule
lowerCamelCase_ = {'''processing_wav2vec2_with_lm''': ['''Wav2Vec2ProcessorWithLM''']}
if TYPE_CHECKING:
from .processing_wavaveca_with_lm import WavaVecaProcessorWithLM
else:
import sys
lowerCamelC... | 253 | 0 |
'''simple docstring'''
from .testing import (
are_the_same_tensors,
execute_subprocess_async,
require_bnb,
require_cpu,
require_cuda,
require_huggingface_suite,
require_mps,
require_multi_gpu,
require_multi_xpu,
require_safetensors,
require_single_gpu,
require_sing... | 58 |
import warnings
from ...utils import logging
from .image_processing_beit import BeitImageProcessor
lowerCAmelCase__ = logging.get_logger(__name__)
class lowerCAmelCase__ ( a):
'''simple docstring'''
def __init__( self , *__lowerCamelCase , **__lowerCamelC... | 11 | 0 |
"""simple docstring"""
import flax.linen as nn
import jax.numpy as jnp
from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD
class _A ( nn.Module ):
"""simple docstring"""
... | 226 |
"""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, prepa... | 226 | 1 |
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 Mo... | 62 |
"""simple docstring"""
from collections import UserDict
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, Pipeli... | 16 | 0 |
'''simple docstring'''
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import VideoMAEConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, t... | 55 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
__snake_case ={
"""configuration_swiftformer""": [
"""SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""S... | 55 | 1 |
"""simple docstring"""
from __future__ import annotations
from typing import Any
class lowerCAmelCase__ :
def __init__( self : Tuple , _lowerCamelCase : int , _lowerCamelCase : int , _lowerCamelCase : float = 0 ):
_snake... | 288 |
"""simple docstring"""
from __future__ import annotations
from bisect import bisect_left
from functools import total_ordering
from heapq import merge
@total_ordering
class lowerCAmelCase__ ( A_ ):
def __lt__( self : Any , _lowerCamelCase : int ... | 288 | 1 |
import argparse
import json
import re
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileNetVaConfig,
MobileNetVaForImageClassification,
MobileNetVaImageProcessor,
load_tf_weights_in_mobilenet_v... | 22 |
import argparse
import os
from pathlib import Path
import fairseq
import torch
from packaging import version
from torch import nn
from transformers import (
BartConfig,
BartForConditionalGeneration,
BartForSequenceClassification,
BartModel,
BartTokenizer,
)
from transformers.utils import log... | 22 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCAmelCase__ = {
"""configuration_longformer""": [
"""LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
... | 68 |
"""simple docstring"""
import inspect
import unittest
from huggingface_hub import hf_hub_download
from transformers import ASTConfig
from transformers.testing_utils import require_torch, require_torchaudio, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_torchaudio_availa... | 132 | 0 |
"""simple docstring"""
from __future__ import annotations
def lowerCAmelCase_( lowercase_ : Dict , lowercase_ : Tuple , lowercase_ : Tuple , lowercase_ : Union[str, Any] ) -> Any: # noqa: E741
while r - l > 1:
_lowerCamelCa... | 367 |
"""simple docstring"""
from __future__ import annotations
from numpy import array, cos, cross, floataa, radians, sin
from numpy.typing import NDArray
def lowerCAmelCase_( lowercase_ : float , lowercase_ : float , lowercase_ : bool = False ) -> list[fl... | 73 | 0 |
from typing import Union
import fire
import torch
from tqdm import tqdm
def UpperCAmelCase__ ( lowerCamelCase, lowerCamelCase = "cpu", lowerCamelCase = None ):
lowercase :Optional[Any] = torch.load(lowerCamelCase, map_location=lowerCamelCase )
for k, v in tqdm(state_dict.items... | 236 |
import math
import sys
def UpperCAmelCase__ ( lowerCamelCase ):
if number != int(lowerCamelCase ):
raise ValueError("the value of input must be a natural number" )
if number < 0:
raise ValueError("the value of input must not be a negative number" )
if number == 0:
... | 236 | 1 |
"""simple docstring"""
lowerCamelCase_ = {
"Pillow": "Pillow",
"accelerate": "accelerate>=0.11.0",
"compel": "compel==0.1.8",
"black": "black~=23.1",
"datasets": "datasets",
"filelock": "filelock",
"flax": "flax>=0.4.1",
"hf-doc-builder": "hf-doc-builder>=0.3.... | 239 |
"""simple docstring"""
import json
import os
from datetime import date
from pathlib import Path
from tabulate import DataRow, TableFormat, tabulate
lowerCamelCase_ = TableFormat(
lineabove=None,
linebelowheader=None,
linebetweenrows=None,
linebelow=None,
headerrow... | 239 | 1 |
"""simple docstring"""
import torch
from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class UpperCamelCase__( __A ):
lowerCAmelCase__ : str = (EulerDiscreteScheduler,)
lowerCAme... | 221 |
"""simple docstring"""
import unittest
from transformers import MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, AutoTokenizer, is_vision_available
from transformers.pipelines import pipeline
from transformers.pipelines.document_question_answering import apply_tesseract
from transformers.testing_utils import (
... | 17 | 0 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
... | 324 |
"""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_sentencepiec... | 324 | 1 |
import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union
import tqdm
from filelock import FileLock
from transformers import (
BartTokenizer,
BartTokenizerFast,
DataProcessor,
PreTrainedTokenizer,
RobertaTokenizer,
RobertaTokenize... | 147 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_lowerCAmelCase : Union[str, Any] = {
"configuration_blenderbot_small": [
"B... | 169 | 0 |
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device
a_ = False
class _lowercase ( unittest.TestCase ):
pass
@nightly
@require_tor... | 50 | import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional
import pyarrow as pa
import pyarrow.json as paj
import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline
a_ = datasets.utils.logging.get_logger(__name__)
@dataclas... | 50 | 1 |
def UpperCamelCase ( _A ):
"""simple docstring"""
if not isinstance(_A, _A ):
raise ValueError("""Input must be an integer""" )
if input_num <= 0:
raise ValueError("""Input must be positive""" )
return sum(
divisor for divis... | 342 |
import unittest
from typing import Dict, List, Optional, Union
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_imag... | 342 | 1 |
"""simple docstring"""
from __future__ import annotations
def _A ( UpperCamelCase_ : float, UpperCamelCase_ : float, UpperCamelCase_ : float, ) -> tuple[str, float]:
'''simple docstring'''
if (stress, tangential_force, area).count(0) != 1:
raise ValueErro... | 144 |
"""simple docstring"""
import json
import os
import tempfile
import datasets
from utils import generate_example_dataset, get_duration
_a = 5_00_00
_a = 50_00
_a , _a = os.path.split(__file__)
_a = os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENA... | 144 | 1 |
import argparse
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt
if __name__ == "__main__":
A : Union[str, Any] = argparse.ArgumentParser()
parser.add_argument(
'--checkpoint_path', default=None, type=str, required=True, help='Path ... | 6 |
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 _UpperCAmelCase ( ... | 207 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE : List[Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE : Any = {
"edbeeching/decision-transformer-gym-hopper-medium": (
"https://hugging... | 252 |
# 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... | 252 | 1 |
class _snake_case :
def __init__( self , _a ):
__magic_name__ : Tuple = size
__magic_name__ : Union[str, Any] = [0] * size
__magic_name__ : int = [0] * size
@staticmethod
def SCREAMING_SNAKE_CASE ( _a ):
return index | (in... | 281 |
def lowerCAmelCase_ ( _snake_case : list[list[int | float]] ) -> int:
'''simple docstring'''
__magic_name__ : Any = len(_snake_case )
__magic_name__ : Optional[Any] = len(matrix[0] )
__magic_name__ : Union[str, Any] = min(_snake_case , _snake_ca... | 281 | 1 |
"""simple docstring"""
from packaging import version
from .import_utils import is_accelerate_available
if is_accelerate_available():
import accelerate
def UpperCAmelCase__ (snake_case__ : Dict ):
"""simple docstring"""
if not is_accelerate_available():
return meth... | 132 |
"""simple docstring"""
from .data_collator import (
DataCollatorForLanguageModeling,
DataCollatorForPermutationLanguageModeling,
DataCollatorForSeqaSeq,
DataCollatorForSOP,
DataCollatorForTokenClassification,
DataCollatorForWholeWordMask,
DataCollatorWithPadding,
D... | 132 | 1 |
from __future__ import annotations
import queue
class A :
def __init__( self, UpperCamelCase__ ):
"""simple docstring"""
lowerCAmelCase_ = data
lowerCAmelCase_ = None
lowerCAmelCase_ = None
def __UpperCamelCase ( ):... | 278 |
#
# This a `torch.distributed` diagnostics script that checks that all GPUs in the cluster (one or
# many nodes) can talk to each other via nccl and allocate gpu memory.
#
# To run first adjust the number of processes and nodes:
#
# python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distr... | 226 | 0 |
from argparse import ArgumentParser
from .add_new_model import AddNewModelCommand
from .add_new_model_like import AddNewModelLikeCommand
from .convert import ConvertCommand
from .download import DownloadCommand
from .env import EnvironmentCommand
from .lfs import LfsCommands
from .pt_to_tf import PTt... | 116 |
import warnings
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFe... | 116 | 1 |
def UpperCAmelCase__ ( _A : list ):
'''simple docstring'''
a__ =len(_A )
for i in range(1 , _A ):
a__ =collection[i]
a__ =0
a__ =i - 1
while low <= high:
a__ =(low + high) // 2
if val < collection[mid]:
a__ =mid - 1
... | 188 |
from collections import UserDict
from typing import Union
import numpy as np
import requests
from ..utils import (
add_end_docstrings,
logging,
)
from .audio_classification import ffmpeg_read
from .base import PIPELINE_INIT_ARGS, Pipeline
lowerCamelCase = logging.get_logger(__name__)
... | 188 | 1 |
"""simple docstring"""
import numpy as np
def lowerCamelCase_ ( _lowerCamelCase ):
return 1 / (1 + np.exp(-vector ))
def lowerCamelCase_ ( _lowerCamelCase ):
return vector * sigmoid(1.702 * vector )
if __name__ == "__main__":
import doctest
doctest.testmod()... | 316 |
"""simple docstring"""
print((lambda quine: quine % quine)("print((lambda quine: quine %% quine)(%r))"))
| 316 | 1 |
'''simple docstring'''
import warnings
warnings.warn(
"memory_utils has been reorganized to utils.memory. Import `find_executable_batchsize` from the main `__init__`: "
"`from accelerate import find_executable_batch_size` to avoid this warning.",
FutureWarning,
)
| 47 |
'''simple docstring'''
lowerCamelCase : Any = "\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"
low... | 47 | 1 |
def __lowerCAmelCase ( a__ , a__ ) -> Tuple:
# "extended trapezoidal rule"
# int(f) = dx/2 * (f1 + 2f2 + ... + fn)
__a = (boundary[1] - boundary[0]) / steps
__a = boundary[0]
__a = boundary[1]
__a = make_points(a__ ... | 33 |
import inspect
import os
import unittest
from dataclasses import dataclass
import torch
from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cuda, require_multi_gpu
from ac... | 33 | 1 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaImgaImgPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, ... | 72 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
lowerCAmelCase__ = {'''configuration_reformer''': ['''REFORMER_PRETRAINED_CONF... | 72 | 1 |
import logging
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
BERT_START_DOCSTRING,
BertEncod... | 358 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils i... | 138 | 0 |
import collections
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCamelCase_ = logging.get_logger(__name__)
UpperCamelCase_ = '''▁'''
UpperCamelCase_ ... | 345 |
import collections
import inspect
import unittest
from transformers import FocalNetConfig
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_backbone_common import BackboneTes... | 345 | 1 |
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Sequence, Value
from .base import TaskTemplate
@dataclass(frozen=lowercase_ )
class __UpperCAmelCase ( lowercase_ ):
# `task` is not a ClassVar since we want it to be part of the `as... | 351 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase : str = logging.get_logger(__name__)
_UpperCAmelCase : int = {
"""google/canine-s""": """https://huggingface.co/google/canine-s/resolve/main/config.json""",
# See all CANINE mode... | 200 | 0 |
import requests
def lowerCamelCase__ ( A__ : str , A__ : str ):
'''simple docstring'''
__lowerCamelCase = {"""Content-Type""": """application/json"""}
__lowerCamelCase = requests.post(A__ , json={"""text""": message_body} , ... | 12 | """simple docstring"""
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image
from transforme... | 289 | 0 |
"""simple docstring"""
import sys
import webbrowser
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
print('''Googling.....''')
snake_case__ : Optional[Any] = '''https://www.google.com/search?q=''' + ''' '''.join(sys.argv[1:])... | 314 |
"""simple docstring"""
import argparse
import requests
import torch
from PIL import Image
from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor
def _snake_case ( _snake_case : List[str] ):
lowerCAmelCase : Union[str, Any] = SwinConfig(image_size... | 314 | 1 |
'''simple docstring'''
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase :Any = logging.get_logger(__name__)
lowerCamelCase :Union[str, Any] = {
'''asapp/sew-tiny-100k''': '''https://huggingf... | 206 |
import os
# Precomputes a list of the 100 first triangular numbers
lowerCAmelCase__ = [int(0.5 * n * (n + 1)) for n in range(1, 1_0_1)]
def __lowerCamelCase ( ):
"""simple docstring"""
lowercase__ : str = os.path.dirname(os.path.realpath(lowerCamelCase__ )... | 130 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__A : int = {
'''configuration_graphormer''': ['''GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GraphormerConfig'''],
}... | 27 |
"""simple docstring"""
def A_ ( snake_case_ : int ):
'''simple docstring'''
if number < 0:
raise ValueError("""number must not be negative""" )
return number & (number - 1) == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 27 | 1 |
import os
import unittest
from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class _lowercase ( A__ , unittest.TestCase ):
'''simple docstring'''
SCREAMING_... | 9 |
'''simple docstring'''
a_ : Any = [
9_99,
8_00,
7_99,
6_00,
5_99,
5_00,
4_00,
3_99,
3_77,
3_55,
3_33,
3_11,
2_88,
2_66,
2_44,
2_22,
2_00,
1_99,
1_77,
1_55,
1_33,
1_11,
88,
66,
44,
22,... | 75 | 0 |
'''simple docstring'''
from __future__ import annotations
import numpy as np
def lowerCAmelCase_ ( snake_case_ : list[float] ) -> List[str]:
'''simple docstring'''
return np.maximum(0 , snake_case_ )
if __name__ == "__main__":
print(np.array(relu([-1, 0, 5])))... | 106 | '''simple docstring'''
def lowerCAmelCase_ ( snake_case_ : int = 1_00_00_00 ) -> int:
'''simple docstring'''
UpperCAmelCase_ = limit + 1
UpperCAmelCase_ = [0] * limit
for first_term in range(1 , snake_case_ ):
for n in range(snake_cas... | 106 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ : Union[str, Any] = logging.get_logger(__name__)
lowercase__ : Tuple = {
"unc-nlp/lxmert-base-uncased": "https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/co... | 328 |
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ : Any = logging.get_logger(__name__)
lowercase__ : Tuple = {
"xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/config.json",
"... | 328 | 1 |
from __future__ import annotations
import unittest
from transformers import 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
from ...test_pipeline... | 354 |
# 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 applicab... | 90 | 0 |
'''simple docstring'''
from __future__ import annotations
from fractions import Fraction
def UpperCAmelCase ( a_ , a_ ) -> bool:
"""simple docstring"""
return (
num != den and num % 1_0 == den // 1_0 and (num // 1_0) / (den % 1_0) == n... | 344 |
'''simple docstring'''
import os
from pathlib import Path
from unittest.mock import patch
import pytest
import zstandard as zstd
from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
OfflineModeIsEnabled,
cached_path,
fsspec_get,... | 344 | 1 |
"""simple docstring"""
import os
import sys
import unittest
__lowercase = 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_model_to_tes... | 353 | """simple docstring"""
from __future__ import annotations
from collections.abc import Iterator
from typing import Any
class _lowercase :
"""simple docstring"""
def __init__( self : int , UpperCamelCase__ : Any ) -> Optional... | 85 | 0 |
from __future__ import annotations
from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass
@dataclass
class A__ :
"""simple docstring"""
__magic_name__ = 42
__magic_name__ = 42
class A__ :
... | 127 |
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
class A__ ( unittest.TestCase ):
... | 127 | 1 |
'''simple docstring'''
import itertools
import os
from collections import Counter, defaultdict
from concurrent.futures import ThreadPoolExecutor, as_completed
import numpy as np
import datasets
from .execute import check_correctness
A : Optional[Any] = '''\
@misc{chen2021evaluating,
... | 227 |
'''simple docstring'''
from __future__ import annotations
class __lowerCamelCase :
"""simple docstring"""
def __init__( self : List[Any] , SCREAMING_SNAKE_CASE : list[list[int]]):
_A : Dict = TypeError(
'Matrices must be fo... | 227 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__A : List[str] = logging.get_logger(__name__)
__A : Tuple =... | 33 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_A = logging.get_logger(__name__)
_A = {
'''google/vit-base-patch16-224''': '''https://hu... | 278 | 0 |
'''simple docstring'''
from ....configuration_utils import PretrainedConfig
from ....utils import logging
lowercase : Union[str, Any] = logging.get_logger(__name__)
lowercase : str = {
"CarlCochet/trajectory-transformer-halfcheetah-medium-v2": (
"https://huggingface.co/CarlC... | 160 |
'''simple docstring'''
import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey
class __UpperCAmelCase :
def __init__( self , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ):
"""simple docstring"""
if dst_width < ... | 160 | 1 |
def _a ( lowerCamelCase: str , lowerCamelCase: Union[str, Any] ) -> int:
'''simple docstring'''
__A = len(lowerCamelCase )
__A = []
for i in range(len(lowerCamelCase ) - pat_len + 1 ):
__A ... | 117 |
'''simple docstring'''
def A_ ( snake_case = 100 ):
SCREAMING_SNAKE_CASE:Optional[Any] = set()
SCREAMING_SNAKE_CASE:int = 0
SCREAMING_SNAKE_CASE:Optional[Any] = n + 1 # maximum limit
for a in range(2 , snake_case ):
for b in ra... | 139 | 0 |
'''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
@requ... | 346 |
'''simple docstring'''
def _lowerCamelCase ( lowercase : int , lowercase : list ) -> Union[str, Any]:
_enforce_args(lowercase , lowercase )
if n == 0:
return 0
_a = float("-inf" )
for i in range(1 , ... | 346 | 1 |
import argparse
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import (
RobertaTokenizer,
TrOCRConfig,
TrOCRForCausalLM,
TrOCRProcessor,
VisionEncoderDecoderModel,
ViTConfig,
ViTImageProcessor,
ViTModel,
)
from transformers.utils import lo... | 175 |
def lowercase_( SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
for i in range(len(SCREAMING_SNAKE_CASE_ ) - 1 , 0 , -1 ):
lowerCamelCase : Tuple = False
for j in range(SCREAMING_SNAKE_CASE_ , 0 , -1 ):
if unsorted[j] < unsorted[j... | 283 | 0 |
def snake_case( __magic_name__ ) -> list:
'''simple docstring'''
if n_term == "":
return []
lowercase : list = []
for temp in range(int(__magic_name__ ) ):
series.append(F"""1/{temp + 1}""" if series else '''1''' )
... | 116 |
def snake_case( __magic_name__ , __magic_name__ , __magic_name__ ) -> int:
'''simple docstring'''
if len(__magic_name__ ) != len(__magic_name__ ):
raise ValueError('''The length of profit and weight must be same.''' )
if max_weight <= 0:
... | 116 | 1 |
import unittest
from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
UpperCAmelCase_ ... | 201 |
UpperCAmelCase_ = 'Input must be a string of 8 numbers plus letter'
UpperCAmelCase_ = 'TRWAGMYFPDXBNJZSQVHLCKE'
def lowerCAmelCase_ ( __UpperCAmelCase: str ) -> bool:
if not isinstance(__UpperCAmelCase , __UpperCAmelCase ):
UpperCamel... | 201 | 1 |
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, is_vision_available
fr... | 350 |
import gc
import random
import unittest
import torch
from diffusers import (
IFImgaImgPipeline,
IFImgaImgSuperResolutionPipeline,
IFInpaintingPipeline,
IFInpaintingSuperResolutionPipeline,
IFPipeline,
IFSuperResolutionPipeline,
)
from diffusers.models.attention_processor import AttnAddedK... | 176 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {
"facebook/timesformer": "https://huggingface.co/facebook/timesformer/resolve/main/config.json",
}
class lowercas... | 0 |
def _a ( a :int ) -> list:
# bit count represents no. of bits in the gray code
if bit_count < 0:
raise ValueError('''The given input must be positive''' )
# get the generated string sequence
a = gray_code_sequence_string(a )
#
# convert them to integers
f... | 0 | 1 |
import json
import os
import unittest
from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class __lowercase ( a_ , unittest.TestCase ):
"""simple docstring"""
UpperCamelCase : ... | 350 |
from typing import Optional
import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class __lowerc... | 66 | 0 |
import unittest
import numpy as np
def A (__A : np.ndarray , __A : np.ndarray , __A : np.ndarray , __A : np.ndarray | None = None , ) -> np.ndarray:
"""simple docstring"""
UpperCAmelCase_ = np.shape(__A... | 51 |
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... | 51 | 1 |
"""simple docstring"""
import string
def lowercase__ ( _UpperCAmelCase ) -> str:
'''simple docstring'''
lowercase : Any = ''
for i in sequence:
lowercase : int = ord(_UpperCAmelCase )
... | 369 |
"""simple docstring"""
_UpperCamelCase: Dict = 2_5_6
# Modulus to hash a string
_UpperCamelCase: Union[str, Any] = 1_0_0_0_0_0_3
def lowercase__ ( _UpperCAmelCase , _UpperCAmelCase ) -> bool:
'''simple docstring''... | 53 | 0 |
import os
import torch
from ..logging import get_logger
from .constants import FSDP_PYTORCH_VERSION, MODEL_NAME, OPTIMIZER_NAME
from .versions import is_torch_version
if is_torch_version('''>=''', FSDP_PYTORCH_VERSION):
import torch.distributed.checkpoint as dist_cp
from torch.distribut... | 39 |
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ ):
snake_case_ = []
if len(SCREAMING_SNAKE_CASE__ ) == 1:
return [nums.copy()]
for _ in range(len(SCREAMING_SNAKE_CASE__ ) ):
snake_case_ = nums.pop(0 )
snake_case_ ... | 8 | 0 |
from collections import UserDict
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 impo... | 352 |
"""simple docstring"""
import numpy as np
import skfuzzy as fuzz
if __name__ == "__main__":
# Create universe of discourse in Python using linspace ()
__UpperCamelCase : List[Any] = np.linspace(start=0, stop=75, num=75, endpoint=True, retstep=False)
# Create two fuzzy sets b... | 309 | 0 |
"""simple docstring"""
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
c... | 293 |
"""simple docstring"""
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_M... | 293 | 1 |
class __SCREAMING_SNAKE_CASE :
def __init__( self ):
lowercase : Optional[Any] = ''''''
lowercase : Any = ''''''
lowercase : Union[str, Any] = []
def __lowerCamelCase ( self ... | 358 |
import json
import os
import shutil
import tempfile
from unittest import TestCase
from transformers import BartTokenizer, BartTokenizerFast, DPRQuestionEncoderTokenizer, DPRQuestionEncoderTokenizerFast
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bert.tokenization_bert... | 173 | 0 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionSAGPipeline,
UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils... | 231 |
from __future__ import annotations
import csv
import requests
from bsa import BeautifulSoup
def lowerCamelCase__ ( __lowerCAmelCase : str = "" ):
"""simple docstring"""
lowerCAmelCase_ = url or "https://www.imdb.com/chart/top/?ref_=nv_mv_250"
lowerCAmelCase_ = BeautifulSo... | 231 | 1 |
"""simple docstring"""
from __future__ import annotations
import sys
from collections import deque
from typing import Generic, TypeVar
A__ : Optional[int] = TypeVar('T')
class lowercase__ ( Generic[T] ):
_UpperCAmelCase :deque[T] # Cache store of keys
_UpperCAmel... | 364 |
"""simple docstring"""
import copy
import json
import os
import tempfile
from transformers import is_torch_available
from .test_configuration_utils import config_common_kwargs
class lowercase__ ( snake_case__ ):
def __init__( self : Tuple , snake_case__ ... | 209 | 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
if is_torch_available():
impor... | 77 | '''simple docstring'''
from typing import List, Union
import numpy as np
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_... | 67 | 0 |
import json
import sys
def __SCREAMING_SNAKE_CASE ( lowerCAmelCase: int , lowerCAmelCase: List[str] ) -> Tuple:
with open(lowerCAmelCase , encoding="utf-8" ) as f:
_UpperCAmelCase : Tuple = json.load(lowerCAmelCase )
_UpperCAmelCase : ... | 360 |
from __future__ import annotations
import math
import random
from collections.abc import Collection
from typing import overload
class a :
def __init__( self , A_ = None ):
'''simple docstring'''
if components is None:
_UpperCAmelCase : Dict ... | 189 | 0 |
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_effective_axis_dimension
from ...u... | 182 | import math
def A ( _lowercase ):
return math.sqrt(_lowercase ) * math.sqrt(_lowercase ) == num
def A ( _lowercase ):
SCREAMING_SNAKE_CASE : Dict = 0
SCREAMING_SNAKE_CASE : Tuple = n
while left <= right:
... | 182 | 1 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, LDMTextToImagePipeline, UNetaDConditionModel
from diffusers.utils.testing_utils import (
enable_full_determinism,
lo... | 367 |
from __future__ import annotations
from collections.abc import Callable
from typing import Generic, TypeVar
a =TypeVar("""T""")
a =TypeVar("""U""")
class A_ ( Generic[T, U] ):
def __init__( self : Tuple ,SCREAMING_SNAKE_CASE__ : T | None ,SCREAMING_SNAKE_... | 113 | 0 |
from math import sqrt
def a ( _UpperCAmelCase : Optional[int] ):
'''simple docstring'''
assert isinstance(_UpperCAmelCase , _UpperCAmelCase ) and (
number >= 0
), "'number' must been an int and positive"
__UpperCAmelCase :... | 226 |
class __lowercase :
"""simple docstring"""
def __init__( self : List[Any] , lowerCAmelCase__ : Optional[int] , lowerCAmelCase__ : List[Any]):
SCREAMING_SNAKE_CASE_: List[str] = name
SCREAMING_SNAKE_CASE_: Union[str, Any] = val
... | 13 | 0 |
from __future__ import annotations
from collections.abc import Iterator
class __SCREAMING_SNAKE_CASE :
def __init__( self , _UpperCamelCase ):
"""simple docstring"""
lowerCAmelCase__ = value
lowerCAmelCase__ = None
... | 122 |
import argparse
import collections
import json
import os
import re
import string
import sys
import numpy as np
__snake_case : Any = re.compile(R"""\b(a|an|the)\b""", re.UNICODE)
__snake_case : List[Any] = None
def _UpperCamelCase ( ) ... | 122 | 1 |
import argparse
from tax import checkpoints
from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM
def __lowercase ( lowerCamelCase : Optional[Any] , lowerCamelCase : Optional[int] , lowerCamelCase : Optional[int] ):
UpperCamelCase_ : Any = AutoConfig... | 175 | import builtins
import sys
from ...utils.imports import _is_package_available
from . import cursor, input
from .helpers import Direction, clear_line, forceWrite, linebreak, move_cursor, reset_cursor, writeColor
from .keymap import KEYMAP
a_ = False
try:
a_ = _is_package_available('goog... | 175 | 1 |
def UpperCamelCase ( __lowerCamelCase : int = 10**12 ):
snake_case : Tuple = 1
snake_case : Any = 0
snake_case : Any = 1
snake_case : str = 1
while numerator <= 2 * min_total - 1:
... | 370 |
import argparse
import fairseq
import torch
from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging
logging.set_verbosity_info()
__lowerCamelCase = logging.get_logger(__name__)
__lowerCamelCase = {
"""post_extract_proj""": """feature_projec... | 10 | 0 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision import transforms
from transformers import BitImageProcessor, FocalNetConfig, FocalNetForImageClassification
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IM... | 207 |
'''simple docstring'''
def a_ ( __snake_case : Any , __snake_case : List[str] ) -> str:
"""simple docstring"""
lowerCamelCase_ =''''''
for i in table:
res += inp[i - 1]
return res
def a_ ( ... | 75 | 0 |
"""simple docstring"""
def SCREAMING_SNAKE_CASE ( _lowerCamelCase : int ,_lowerCamelCase : int ) -> int:
return 1 if input_a == input_a else 0
def SCREAMING_SNAKE_CASE ( ) -> None:
assert xnor_gate(0 ,0 ) == 1
assert xnor_gate(0 ,1 ... | 351 | """simple docstring"""
import socket
def SCREAMING_SNAKE_CASE ( ) -> str:
_lowerCAmelCase : Optional[int] = socket.socket(socket.AF_INET ,socket.SOCK_STREAM )
_lowerCAmelCase : Optional[int] = socket.gethostname()
_lowerCAmelCase : Tuple ... | 126 | 0 |
'''simple docstring'''
import contextlib
from multiprocessing import Pool, RLock
from tqdm.auto import tqdm
from ..utils import experimental, logging
A__ : Any =logging.get_logger(__name__)
class UpperCAmelCase :
_lowercase: Optional[Any] =... | 70 |
def _UpperCamelCase ( lowercase__ , lowercase__ , lowercase__=False ):
if isinstance(lowercase__ , lowercase__ ) and isinstance(lowercase__ , lowercase__ ):
__SCREAMING_SNAKE_CASE : List[str] = len(set_a.intersectio... | 9 | 0 |
"""simple docstring"""
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelerate... | 357 |
"""simple docstring"""
__UpperCamelCase : Dict = {0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}
__UpperCamelCase : str = {0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : dict[int, list[int]] , _UpperCAmelCase : ... | 309 | 0 |
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