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'''
lowerCamelCase = 8.3_1_4_4_5_9_8
def _A ( _lowerCAmelCase , _lowerCAmelCase ):
"""simple docstring"""
if temperature < 0:
raise Exception('Temperature cannot be less than 0 K' )
if molar_mass <= 0:
... | 166 |
'''simple docstring'''
import unittest
from transformers import AlbertTokenizer, AlbertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
lowerCamelCase = get_tests_dir("""... | 166 | 1 |
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.jax_utils import replicate
... | 60 |
import ast
import os
import re
import shutil
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.test_utils.examples import compare_against_test
from accelerate.test_utils.testing import TempDirTestCase, require_trackers, run_command, slow
from accelerate.utils impor... | 60 | 1 |
"""simple docstring"""
from datetime import datetime
import requests
def lowercase__ ( snake_case_ :str ):
__UpperCAmelCase = '''https://downloadgram.net/wp-json/wppress/video-downloader/video?url='''
__UpperCAmelCase = requests.get(base_url + url ).json()... | 332 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import _LazyModule
_lowercase : int = {'processing_wav2vec2_with_lm': ['Wav2Vec2ProcessorWithLM']}
if TYPE_CHECKING:
from .processing_wavaveca_with_lm import WavaVecaProcessorWithLM
else:
import sys
... | 332 | 1 |
"""simple docstring"""
from __future__ import annotations
import numpy as np
def __lowerCAmelCase ( lowercase : list[float] ) -> Any:
"""simple docstring"""
return np.maximum(0 , lowercase )
if __name__ == "__main__":
print(np.array(relu([-1, 0, 5])... | 112 |
"""simple docstring"""
def __lowerCAmelCase ( lowercase : int , lowercase : int , lowercase : list[list[int]] ) -> int:
"""simple docstring"""
def update_area_of_max_square(lowercase : int , lowercase : int ) -> int:
# BASE... | 112 | 1 |
'''simple docstring'''
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... | 1 |
import fire
from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer
def __lowerCamelCase ( lowerCamelCase__ : str , lowerCamelCase__ : str , **lowerCamelCase__ : Tuple ):
'''simple docstring'''
lowerCamelCase = AutoConfig.from_pret... | 252 | 0 |
import math
import unittest
def _lowerCamelCase( lowercase__ ) -> bool:
'''simple docstring'''
assert isinstance(lowerCAmelCase__ , lowerCAmelCase__ ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2 and 3 are primes
... | 361 |
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
lowerCAmelCase = False
class A ( unittest.TestCase ):
pass
@... | 304 | 0 |
'''simple docstring'''
import html
from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from ...utils import is_bsa_available, logging, requires_backends
if is_bsa_available():
import bsa
from bsa import BeautifulSoup
__lowerCAmelCase = logging.get_logger(__name__)
... | 271 |
from math import loga
def A_ ( A__ ) -> int:
if a < 0:
raise ValueError('Input value must be a positive integer' )
elif isinstance(A__ , A__ ):
raise TypeError('Input value must be a \'int\' type' )
return 0 if (a == 0) else int(loga(a & -a ... | 99 | 0 |
'''simple docstring'''
import argparse
import torch
from transformers import (
SpeechTaConfig,
SpeechTaFeatureExtractor,
SpeechTaForSpeechToSpeech,
SpeechTaForSpeechToText,
SpeechTaForTextToSpeech,
SpeechTaProcessor,
SpeechTaTokenizer,
logging,
)
from tra... | 246 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import BlenderbotSmallConfig, BlenderbotSmallTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ... | 246 | 1 |
"""simple docstring"""
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, BertTok... | 17 |
import itertools
import random
import unittest
import numpy as np
from transformers import BatchFeature, SpeechTaFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import SequenceFe... | 273 | 0 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
lowercase : int = logging.get_logger(__name__)
lowercase : Union[str, Any] = {
'ut/deta': 'https://huggingface.co/ut/deta/resolve/main/config.json'... | 370 |
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 Accele... | 151 | 0 |
'''simple docstring'''
import os
import unittest
from transformers.models.phobert.tokenization_phobert import VOCAB_FILES_NAMES, PhobertTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class __lowerCAmelCase (lowerCamelCase_ , unittest.TestCase ):
'''simple docstri... | 2 |
'''simple docstring'''
_snake_case = frozenset(
[
'prompt',
'height',
'width',
'guidance_scale',
'negative_prompt',
'prompt_embeds',
'negative_prompt_embeds',
'cross_attention_kwargs',
]
)
_snake_case = frozenset(['prompt', 'negative_p... | 250 | 0 |
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_backbone_common ... | 171 |
def UpperCAmelCase_ (_lowerCAmelCase : int = 1_00 ):
__UpperCamelCase : Tuple = n * (n + 1) * (2 * n + 1) / 6
__UpperCamelCase : List[str] = (n * (n + 1) / 2) ** 2
return int(square_of_sum - sum_of_squares )
if __name__ == "__main__":
print(F"""{s... | 171 | 1 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
A__ = {'tokenization_bertweet': ['BertweetTokenizer']}
if TYPE_CHECKING:
from .tokenization_bertweet import BertweetTokenizer
else:
import sys
A__ = _LazyModule(__name__, globals()['''__fil... | 230 |
"""simple docstring"""
import requests
from bsa import BeautifulSoup
def _SCREAMING_SNAKE_CASE ( __snake_case : str = "AAPL" ):
'''simple docstring'''
lowercase = f'https://in.finance.yahoo.com/quote/{symbol}?s={symbol}'
lowercase = BeautifulSoup(... | 220 | 0 |
"""simple docstring"""
import sys
import turtle
def lowercase ( A_ , A_ )-> tuple[float, float]:
'''simple docstring'''
return (pa[0] + pa[0]) / 2, (pa[1] + pa[1]) / 2
def lowercase ( A_ , A_ , A_ , A_ , ... | 352 |
"""simple docstring"""
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ConvNextConfig, SegformerImageProcessor, UperNetConfig, UperNetForSemanticSegmentation
def lowercase ( ... | 226 | 0 |
import argparse
import os
import re
import packaging.version
a_ = '''examples/'''
a_ = {
'''examples''': (re.compile(r'^check_min_version\("[^"]+"\)\s*$', re.MULTILINE), '''check_min_version("VERSION")\n'''),
'''init''': (re.compile(r'^__version__\s+=\s+"([^"]+)"\s*$', re.MULTILIN... | 175 |
"""simple docstring"""
from typing import Dict, List, Optional, Tuple, Union
import torch
from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class ... | 264 | 0 |
'''simple docstring'''
__lowerCAmelCase = [
(1_000, '''M'''),
(900, '''CM'''),
(500, '''D'''),
(400, '''CD'''),
(100, '''C'''),
(90, '''XC'''),
(50, '''L'''),
(40, '''XL'''),
(10, '''X'''),
(9, '''IX'''),
(5, '''V'''),
(4, '''IV'''),
(1, '''I''')... | 359 |
'''simple docstring'''
from binascii import hexlify
from hashlib import shaaaa
from os import urandom
# RFC 3526 - More Modular Exponential (MODP) Diffie-Hellman groups for
# Internet Key Exchange (IKE) https://tools.ietf.org/html/rfc3526
__lowerCAmelCase = {
# 1536-bit
5: {
'''p... | 107 | 0 |
import os
import socket
from contextlib import contextmanager
import torch
from ..commands.config.default import write_basic_config # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
from .transformer_engi... | 322 |
import os
from datetime import datetime as dt
from github import Github
_a = [
'''good first issue''',
'''feature request''',
'''wip''',
]
def _a ( ) -> List[Any]:
"""simple docstring"""
__lowerCAmelCase: Dict = Github(os.envi... | 322 | 1 |
'''simple docstring'''
import inspect
import os
import torch
from transformers import AutoModel
from transformers.testing_utils import mockenv_context
from transformers.trainer_utils import set_seed
import accelerate
from accelerate.accelerator import Accelerator
from accelerate.state import AcceleratorState
from... | 229 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowercase = {
"""configuration_table_transformer""": [
"""TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""TableTransformerConfig""",
... | 229 | 1 |
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart import BartTokeniz... | 226 |
import unittest
from transformers import (
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TextClassificationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow
from .te... | 32 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_a : List[Any]= logging.get_logger(__name__)
_a : List[Any]= {
"ksst... | 95 | """simple docstring"""
from __future__ import annotations
import math
def __UpperCAmelCase ( UpperCAmelCase_ : int ) -> bool:
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == ... | 95 | 1 |
'''simple docstring'''
from abc import ABC, abstractmethod
from typing import Optional, Union
from .. import Dataset, DatasetDict, Features, IterableDataset, IterableDatasetDict, NamedSplit
from ..utils.typing import NestedDataStructureLike, PathLike
class A_ ( lowerCAmelCase_ ):
def __init_... | 22 |
'''simple docstring'''
import unittest
from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import T... | 22 | 1 |
import argparse
import torch
from transformers import YosoConfig, YosoForMaskedLM
def SCREAMING_SNAKE_CASE ( lowercase_ ) -> str:
"""simple docstring"""
if "model" in orig_key:
A__ = orig_key.replace('''model.''' , '''''' )
if "norm1" ... | 231 |
from __future__ import annotations
from collections import Counter
from random import random
class UpperCamelCase_ :
'''simple docstring'''
def __init__( self : Any) ->Optional[Any]:
'''simple docstring'''
A__ = {}
def SCREAMING_SN... | 231 | 1 |
from __future__ import annotations
def lowerCAmelCase_ ( snake_case_,snake_case_,snake_case_ ):
if days_between_payments <= 0:
raise ValueError("""days_between_payments must be > 0""" )
if daily_interest_rate < 0:
raise ValueError("""daily_interest_rate mu... | 26 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version('''>=''', '''4.25.0''')):
raise OptionalDependencyNotAva... | 278 | 0 |
from typing import List, Optional, TypeVar
from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _inter... | 363 |
from typing import List, Optional, Union
import torch
from transformers import (
XLMRobertaTokenizer,
)
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDIMScheduler, DDPMSchedule... | 34 | 0 |
'''simple docstring'''
import inspect
import tempfile
from collections import OrderedDict, UserDict
from collections.abc import MutableMapping
from contextlib import ExitStack, contextmanager
from dataclasses import fields
from enum import Enum
from typing import Any, ContextManager, List, Tuple
import numpy ... | 75 | import re
import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey
from ..utils import logging
__lowercase = logging.get_logger(__name__)
def lowerCamelCase ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__UpperCamelCase ... | 43 | 0 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
lowerCAmelCase__ : str =logging.get_logger(__name__)
lowerCAmelCase__ :... | 355 |
def __lowercase ( a__ ) -> int:
__SCREAMING_SNAKE_CASE = [[0 for _ in range(a__ )] for _ in range(m + 1 )]
for i in range(m + 1 ):
__SCREAMING_SNAKE_CASE = 1
for n in range(m + 1 ):
for k in range(1 ... | 118 | 0 |
"""simple docstring"""
import unittest
import numpy as np
from datasets import load_dataset
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, p... | 241 |
"""simple docstring"""
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# 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
#
... | 241 | 1 |
"""simple docstring"""
import importlib
import os
import sys
# This is required to make the module import works (when the python process is running from the root of the repo)
sys.path.append(""".""")
def SCREAMING_SNAKE_CASE__ ( snake_case : Tuple )-> Tuple:
'''simp... | 298 |
"""simple docstring"""
import json
import os
import unittest
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class lowerCAmelCase__ ( __magic_name__ , unitt... | 298 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
_SCREAMING_SNAKE_CASE : List[str] = {
'configuration_r... | 183 |
from __future__ import annotations
import numpy as np
from numpy import floataa
from numpy.typing import NDArray
def __magic_name__ ( A : NDArray[floataa], A : NDArray[floataa], A : list[int], A : int, ):
'''simple docstring'''
a , a = coefficient_matr... | 107 | 0 |
import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union
from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings
a = logging.getLogger(__name__)
... | 358 |
"""simple docstring"""
from dataclasses import dataclass
from typing import Dict, Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .attention_processor import AttentionProcessor, At... | 271 | 0 |
from itertools import product
def _A ( SCREAMING_SNAKE_CASE__ : List[Any] , SCREAMING_SNAKE_CASE__ : Optional[Any] ):
UpperCamelCase :Union[str, Any] = sides_number
UpperCamelCase :Optional[int] = max_face_number * dice_number
UpperCamelCase :... | 259 |
"""simple docstring"""
from __future__ import annotations
def lowercase_ ( __UpperCAmelCase ) -> int:
if not nums:
return 0
lowerCAmelCase__ : List[Any] = nums[0]
lowerCAmelCase__ : List[str] = 0
for num in nums[1:]:
... | 242 | 0 |
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from PIL import Image
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMSched... | 366 |
_SCREAMING_SNAKE_CASE = {
"""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""",
"... | 88 | 0 |
import torch
from diffusers import UnCLIPScheduler
from .test_schedulers import SchedulerCommonTest
class lowercase__ ( UpperCamelCase_):
UpperCamelCase_ = (UnCLIPScheduler,)
def __A ( self : str , **UpperCamelCase__ : List[Any] ):
... | 182 | from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
__UpperCamelCase : Tuple = {
'configuration_swiftformer': [
'SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'SwiftFormerConfig',
... | 182 | 1 |
def lowerCamelCase_ ():
return [
a * b * (1000 - a - b)
for a in range(1 , 999 )
for b in range(_SCREAMING_SNAKE_CASE , 999 )
if (a * a + b * b == (1000 - a - b) ** 2)
][0]
if __name__ == "__main__":
print(f"{solution() = }")
| 351 |
"""simple docstring"""
import qiskit
def lowerCamelCase_ (UpperCamelCase__ : int , UpperCamelCase__ : int ):
_UpperCAmelCase : List[str] = qiskit.Aer.get_backend('''aer_simulator''' )
# Create a Quantum Circuit acting on the q register
_UpperCAmelCase... | 68 | 0 |
"""simple docstring"""
import argparse
import tensorflow as tf
import torch
from transformers import BertConfig, BertForMaskedLM
from transformers.models.bert.modeling_bert import (
BertIntermediate,
BertLayer,
BertOutput,
BertPooler,
BertSelfAttention,
BertSelfOutput,
)
from transfor... | 179 |
"""simple docstring"""
# 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... | 179 | 1 |
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING:
import torch
class _SCREAMING_SNAKE_CASE ( ... | 350 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase_ : Optional[int] = {
'''configuration_jukebox''': [
'''JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''JukeboxConfig''',
'''JukeboxPriorCo... | 62 | 0 |
import os
import unittest
from tempfile import TemporaryDirectory
import torch
import torch.nn as nn
from accelerate.utils import (
OffloadedWeightsLoader,
extract_submodules_state_dict,
load_offloaded_weight,
offload_state_dict,
offload_weight,
)
class _SCREAMING_SNAKE_CASE ( ... | 38 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
a__ : int = {
'configuration_layoutlmv3': [
... | 349 | 0 |
"""simple docstring"""
import inspect
import unittest
import numpy as np
from transformers import BeitConfig
from transformers.testing_utils import require_flax, require_vision, slow
from transformers.utils import cached_property, is_flax_available, is_vision_available
from ...test_configuration_common impo... | 320 |
"""simple docstring"""
import importlib
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Union
import torch
from ..utils import BaseOutput
UpperCAmelCase : Optional[Any] = 'scheduler_config.json'
class lowerCamelCase__ ... | 320 | 1 |
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = {
"""A""": """.-""", """B""": """-...""", """C""": """-.-.""", """D""": """-..""", """E""": """.""", """F""": """..-.""", """G""": """--.""",
"""H""": """....""", """I""": """..""", """J""": """.---""", """K""": """-.-""", """L""": "... | 102 |
"""simple docstring"""
def lowercase ( _snake_case : int , _snake_case : int ) ->str:
"""simple docstring"""
if a < 0 or b < 0:
raise ValueError('''the value of both inputs must be positive''' )
__snake_case : Tuple = str(bin(_snake_case ) ... | 102 | 1 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConformerConfig,
WavaVecaConformerForCTC,
WavaVecaConformerForPreTraining,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
Wava... | 37 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_imagegpt import ImageGPTImageProcessor
lowerCAmelCase__ : Optional[Any] = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE__ ( snake_case__ ):
"""simple docstring"""
... | 37 | 1 |
class UpperCAmelCase :
'''simple docstring'''
def __init__( self : List[str] , lowerCAmelCase_ : Any ):
"""simple docstring"""
_A: Optional[Any] = arr.split(''',''' )
def __magic_name__ ( self : Optional[Any] ):
... | 121 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings
__snake_case = r'''
[`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and
can be used to control the model outputs. Read... | 310 | 0 |
"""simple docstring"""
from __future__ import annotations
def lowerCamelCase__ ( __snake_case, __snake_case ) -> list[int]:
"""simple docstring"""
_UpperCamelCase = 0
_UpperCamelCase = len(__snake_case ) - 1
whi... | 100 |
"""simple docstring"""
import numpy
class _UpperCAmelCase:
def __init__( self , __a , __a) -> None:
'''simple docstring'''
_UpperCamelCase = input_array
# Random initial weights are assigned where first argument is the
... | 100 | 1 |
import os
from datetime import datetime as dt
from github import Github
_UpperCAmelCase : Optional[Any] = [
"good first issue",
"good second issue",
"good difficult issue",
"enhancement",
"new pipeline/model",
"new scheduler",
"wip",
]
def A ( ) -> int... | 222 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_UpperCAmelCase : Union[str, Any] = {
"configuration_instructblip": [
"INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
"InstructBlipConfig",
"InstructBlipQFor... | 222 | 1 |
"""simple docstring"""
import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey
class _UpperCAmelCase :
'''simple docstring'''
def __init__(self , a_ , a_ , a_ ):
'''simple docstring'''
if dst_width < 0 or dst_height < 0:
... | 368 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class _UpperCAmelCase ( metaclass=__snake_case ):
'''simple docstring'''
lowerCamelCase__ =['transformers', 'torch', 'note_seq']
def __init__(self , *a_ , **a_ ):
'''simple docstring'... | 24 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__A = {
"configuration_convbert": ["CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ConvBertConfig", "ConvBertOnnxCon... | 10 |
from typing import Any
def lowerCAmelCase_ ( __a , __a , __a , __a , __a , ) -> list:
"""simple docstring"""
_validation(
__a , __a , __a , __a , __a , )
# Creates data structures and fill initial step
lowerCamelCase__: dict ={}... | 10 | 1 |
import unittest
from huggingface_hub import hf_hub_download
from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VideoMAEFeatureExtractor
from transformers.pipelines import VideoClassificationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
requ... | 185 |
from __future__ import annotations
import unittest
from transformers import BlenderbotConfig, BlenderbotTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from .... | 185 | 1 |
import comet # From: unbabel-comet
import torch
import datasets
__lowerCAmelCase = datasets.logging.get_logger(__name__)
__lowerCAmelCase = """\
@inproceedings{rei-EtAl:2020:WMT,
author = {Rei, Ricardo and Stewart, Craig and Farinha, Ana C and Lavie, Alon},
... | 196 | """simple docstring"""
def _lowerCamelCase( a = 1_0_0_0 ):
__a = 3
__a = 0
while a < n:
if a % 3 == 0 or a % 5 == 0:
result += a
elif a % 1_5 == 0:
result -= a
a += 1
return re... | 261 | 0 |
def lowerCAmelCase__ ( lowerCamelCase : list ,lowerCamelCase : list ,lowerCamelCase : int ):
_A : Dict = len(lowerCamelCase )
_A : List[Any] = [[0] * n for i in range(lowerCamelCase )]
for i in range(lowerCamelCase ... | 357 |
'''simple docstring'''
import uuid
from typing import Any, Dict, List, Optional, Union
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
if is_torch_available():
import tor... | 227 | 0 |
"""simple docstring"""
UpperCAmelCase__ : Tuple = frozenset(
[
'prompt',
'height',
'width',
'guidance_scale',
'negative_prompt',
'prompt_embeds',
'negative_prompt_embeds',
'cross_attention_kwargs',
]
)
Upp... | 25 |
"""simple docstring"""
def lowercase_ ( _snake_case ,_snake_case ):
if not (isinstance(_snake_case ,_snake_case ) and isinstance(_snake_case ,_snake_case )):
raise ValueError("""longest_common_substring() takes two strings for inputs""" )
SCREAM... | 25 | 1 |
from __future__ import annotations
def _A ( lowerCAmelCase_ : List[str] , lowerCAmelCase_ : Optional[int] ):
"""simple docstring"""
lowerCAmelCase__ = 0
lowerCAmelCase__ = len(__snake_case ) - 1
while i < j:
... | 362 |
import collections.abc
from typing import Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithNoAttention, ImageC... | 221 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_UpperCAmelCase : Any = {"""configuration_ibert""": ["""IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """IBertConfig""", """IBertOnnxConfig"""]}
try:
... | 174 |
'''simple docstring'''
from typing import List
import datasets
from datasets.tasks import AudioClassification
from ..folder_based_builder import folder_based_builder
_SCREAMING_SNAKE_CASE = datasets.utils.logging.get_logger(__name__)
class lowerCAmelCase_ ( folder_based_builde... | 158 | 0 |
'''simple docstring'''
import unittest
from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import Tokeniz... | 18 | '''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 OptionalDependencyNotAvail... | 18 | 1 |
from __future__ import annotations
from sys import maxsize
from typing import Generic, TypeVar
_snake_case : str = TypeVar("T")
def lowerCAmelCase_ ( __lowerCamelCase ):
return (position - 1) // 2
def lowerCAmelCase_ ( __lowerCamelCase ):
return (2 *... | 123 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConfig,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaForCTC,
WavaVecaForPreTraining,
WavaVecaProcessor,
logging,
)
from transformers.models.... | 275 | 0 |
"""simple docstring"""
import inspect
import unittest
from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_config... | 354 |
"""simple docstring"""
def snake_case ( A__ ):
UpperCAmelCase_ : Optional[Any] = 0
for ch in input_str:
UpperCAmelCase_ : Tuple = ord(A__ )
UpperCAmelCase_ : Dict = pow(2 ,A__ )
# If we already turned on bit ... | 253 | 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_bert import BertTokenizer
__lowerCAmelCase = logging.get_logger(__name__)
__lowerCAm... | 196 |
from __future__ import annotations
def _lowerCamelCase( lowercase__ , lowercase__ ) -> Any:
'''simple docstring'''
if len(lowercase__ ) <= 1 or n <= 1:
return
insert_next(lowercase__ , n - 1 )
rec_insertion_sort(lowercase__ , n - 1 )
def _lowerCamelCase( lo... | 295 | 0 |
from __future__ import annotations
import math
import numpy as np
from numpy.linalg import norm
def snake_case ( snake_case__ :np.ndarray , snake_case__ :np.ndarray):
return math.sqrt(sum(pow(a - b , 2) for a, b in zip(snake_case__ , snake_case__))... | 352 | import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def snake_case ( ) -> List[Any]:
_A = ArgumentParser(
description=(
"""PyTorch TPU... | 81 | 0 |
def a__ ( UpperCAmelCase : int ) -> str:
if len(SCREAMING_SNAKE_CASE__ ) <= 1:
return lst
UpperCAmelCase : Optional[int] = 1
while i < len(SCREAMING_SNAKE_CASE__ ):
if lst[i - 1] <= lst[i]:
i += 1
else:
UpperCAmelCase , UpperCAmelCase ... | 336 |
import os
import re
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ ... | 8 | 0 |
"""simple docstring"""
import argparse
import os
import torch
from transformers import FlavaConfig, FlavaForPreTraining
from transformers.models.flava.convert_dalle_to_flava_codebook import convert_dalle_checkpoint
def _snake_case ( lowercase__ : Tuple ) -> int:
'''simple docstring'... | 366 |
"""simple docstring"""
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 ...model... | 1 | 0 |
'''simple docstring'''
import argparse
import torch
from transformers import YosoConfig, YosoForMaskedLM
def __snake_case ( UpperCAmelCase_ : Tuple ):
if "model" in orig_key:
lowerCamelCase_ = orig_key.replace("model." , "" )
if "norm1" in orig_key:
... | 55 |
'''simple docstring'''
import argparse
import collections
import json
import os
import re
import string
import sys
import numpy as np
a__ : Optional[int] = re.compile(R'\b(a|an|the)\b', re.UNICODE)
a__ : int = None
def _UpperCamelCase ( ) -> Dict... | 80 | 0 |
import os
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_doctest_list.py
UpperCamelCase__ : str = '.'
if __name__ == "__main__":
UpperCamelCase__ : List[str] = os.path.join(REPO_PATH, """ut... | 367 |
import re
def SCREAMING_SNAKE_CASE__ ( snake_case_ ) -> str:
"""simple docstring"""
if len(re.findall('''[ATCG]''', snake_case_ ) ) != len(snake_case_ ):
raise ValueError('''Invalid Strand''' )
return dna.translate(dna.maketrans('''AT... | 330 | 0 |
from typing import Callable, List, Optional, Tuple, Union
import torch
from transformers import CLIPTextModel, CLIPTokenizer
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin, TransformeraDModel, VQModel
from ...schedulers import VQDiffusionScheduler
from ...utils ... | 123 |
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 enable_full_determin... | 123 | 1 |
'''simple docstring'''
import math
from numpy import inf
from scipy.integrate import quad
def __magic_name__( lowerCamelCase):
if num <= 0:
raise ValueError('''math domain error''')
return quad(lowerCamelCase, 0, lowerCamelCase, args=(lowerCamelCase))[0... | 358 |
'''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 TokenizerTest... | 9 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase = logging.get_logger(__name__)
UpperCamelCase = {
'''funnel-transformer/small''': '''https://huggingface.co/funnel-transformer/small/resolve/main/config.json''',
'''funnel-transfo... | 87 |
'''simple docstring'''
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import datasets
import datasets.config
from .utils import require_beam
class A ( datasets.BeamBasedBuilder ):
'''simpl... | 298 | 0 |
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 GenerationTesterMixin
from ...test_configuration_common import ... | 367 |
def __a ( lowerCAmelCase_ : Dict ) -> Dict:
'''simple docstring'''
return [
{
0: [1, 2],
1: [0, 2],
2: [0, 1, 3, 5],
3: [2, 4],
4: [3],
5: [2, 6, 8],
6: [5, 7],
... | 277 | 0 |
'''simple docstring'''
def __snake_case ( UpperCAmelCase_ : int = 50000000 ):
lowerCamelCase_ = set()
lowerCamelCase_ = int((limit - 24) ** (1 / 2) )
lowerCamelCase_ = set(range(3 , prime_square_limit + 1 , 2 ) )
primes.add(2 )
for p in range(... | 55 |
'''simple docstring'''
import math
def __snake_case ( UpperCAmelCase_ : float , UpperCAmelCase_ : float ):
return math.pow(UpperCAmelCase_ , 2 ) - a
def __snake_case ( UpperCAmelCase_ : float ):
return 2 * x
d... | 55 | 1 |
'''simple docstring'''
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
_lowercase : Optional[Any] = logging.get_logger(__name__)
class UpperCamelCase__( lowerCAmelCase ):
... | 91 |
'''simple docstring'''
from __future__ import annotations
from typing import Any
class UpperCamelCase__:
def __init__( self : Any , lowerCAmelCase : int , lowerCAmelCase : int , lowerCAmelCase : float = 0 )-> None:
"""simple docstring""... | 91 | 1 |
import os
def UpperCAmelCase_ ( ) -> str:
SCREAMING_SNAKE_CASE_ = os.path.dirname(os.path.realpath(__UpperCAmelCase ) )
SCREAMING_SNAKE_CASE_ = os.path.join(__UpperCAmelCase , 'triangle.txt' )
with open(__UpperCAmelCase ) as f:
... | 225 |
class lowerCamelCase_ :
'''simple docstring'''
def __init__( self : List[Any] ):
SCREAMING_SNAKE_CASE_ = {}
def lowerCAmelCase_ ( self : List[str] ):
print(self.vertex )
for i in self.vertex:
print(_lowerCAm... | 225 | 1 |
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
if TYPE_CHECKING:
f... | 370 | from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_SCREAMING_SNAKE_CASE = {"""configuration_yolos""": ["""YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP""", """YolosConfig""", """YolosOnnxConfig"""]}
try:
if not is_visio... | 165 | 0 |
'''simple docstring'''
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 Mod... | 97 |
import unittest
import numpy as np
from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
load_numpy,
nightly,
require_onnxruntime,
require_torch_gpu,
)
if is_onnx_available():
import onnxruntime as or... | 225 | 0 |
lowerCAmelCase__ : int =[
'Audio',
'Array2D',
'Array3D',
'Array4D',
'Array5D',
'ClassLabel',
'Features',
'Sequence',
'Value',
'Image',
'Translation',
'TranslationVariableLanguages',
]
from .audio import Audio
from .features import ArrayaD,... | 162 |
def a__ ( A__ ):
SCREAMING_SNAKE_CASE_ : int = []
SCREAMING_SNAKE_CASE_ : int = []
SCREAMING_SNAKE_CASE_ : Optional[int] = {
'^': 3,
'*': 2,
'/': 2,
'%': 2,
'+': 1,
'-': 1,
} # Prio... | 162 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
SCREAMING_SNAKE_CASE :Dict = {
'configuration_transfo_xl': ['TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TransfoXLConfig'],
'tokenization_transfo_xl': ['Tran... | 15 |
import io
import json
import unittest
from parameterized import parameterized
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu
__UpperCAmelCase = get_tests_... | 119 | 0 |
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 OptionalDependencyNotAvailable:
from ...ut... | 354 |
import unittest
from transformers import is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_availab... | 169 | 0 |
"""simple docstring"""
from typing import Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
fro... | 61 |
"""simple docstring"""
import argparse
import json
import pickle
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transfo... | 61 | 1 |
from __future__ import annotations
import unittest
import numpy as np
from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelT... | 189 |
def __SCREAMING_SNAKE_CASE ( lowerCAmelCase: str , lowerCAmelCase: str ) -> bool:
_UpperCAmelCase : Optional[Any] = len(lowerCAmelCase ) + 1
_UpperCAmelCase : Optional[int] = len(lowerCAmelCase ) + 1
# dp is a 2d matrix where dp[i][j]... | 189 | 1 |
'''simple docstring'''
from __future__ import annotations
from PIL import Image
# Define glider example
a : Optional[int] = [
[0, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0],
[1, 1, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, ... | 56 |
# 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
#
# U... | 189 | 0 |
"""simple docstring"""
import argparse
import glob
import importlib.util
import os
import re
import black
from doc_builder.style_doc import style_docstrings_in_code
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
SCREAMI... | 230 |
"""simple docstring"""
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class UpperCAmelCase_ ( A_ ):
lowercase__ = ['''image_processor''', '''tokenizer''']
lowercase__ = '''ViTImageProcessor'''
... | 230 | 1 |
from typing import Any
class A_ :
def __init__(self :Tuple , _UpperCamelCase :Any )-> Dict:
__A = data
__A = None
def __repr__(self :List[Any] )-> str:
return f"""Node({self.data})"""
cla... | 117 |
# 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 r... | 117 | 1 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_flava import FlavaImageProcessor
a_ = logging.get_logger(__name__)
class snake_case ( _UpperCamelCase):
def __init__( self : Optional[int] , *a__ : str ... | 163 |
"""simple docstring"""
import json
import os
import unittest
from transformers.models.gptsan_japanese.tokenization_gptsan_japanese import (
VOCAB_FILES_NAMES,
GPTSanJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import... | 163 | 1 |
def A_ ( _UpperCAmelCase ):
SCREAMING_SNAKE_CASE_: list[list[int]] = [[0 for _ in range(_UpperCAmelCase )] for _ in range(m + 1 )]
for i in range(m + 1 ):
SCREAMING_SNAKE_CASE_: List[str] = 1
for n in range(m + 1 ):
for k in range(... | 13 |
import os
import tempfile
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from torch import nn
from transformers import (
Adafactor,
AdamW,
get_constant_schedule,
... | 13 | 1 |
"""simple docstring"""
def __UpperCAmelCase ( __lowerCamelCase ) -> Any:
lowercase__ : str = []
lowercase__ : Tuple = []
lowercase__ : int = {
'''^''': 3,
'''*''': 2,
'''/''': 2,
'''%''': 2... | 302 |
"""simple docstring"""
import math
def __UpperCAmelCase ( __lowerCamelCase ) -> str:
lowercase__ : Tuple = 0
lowercase__ : Tuple = 0
while num > 0:
lowercase__ : int = num % 8
lowercase__ : Tu... | 302 | 1 |
import argparse
import json
import torch
from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Optional[Any] , SCREAMING_SNAKE_CASE_: List[str]=1 ) -> str:
'''simple docstring'''
if n_shave_prefix_s... | 68 |
import gc
import unittest
import torch
from parameterized import parameterized
from diffusers import AutoencoderKL
from diffusers.utils import floats_tensor, load_hf_numpy, require_torch_gpu, slow, torch_all_close, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.test... | 68 | 1 |
'''simple docstring'''
def _lowerCAmelCase ( __snake_case : str ) -> str:
if not all(char in '01' for char in bin_string ):
raise ValueError('Non-binary value was passed to the function' )
if not bin_string:
raise ValueError('Empty ... | 190 |
'''simple docstring'''
import argparse
lowercase__ : Any = '''docs/source/_static/js/custom.js'''
def _lowerCAmelCase ( __snake_case : Union[str, Any] ) -> str:
with open(__snake_case , encoding='utf-8' , newline='\n' )... | 190 | 1 |
'''simple docstring'''
import argparse
import os
import re
import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTens... | 63 |
'''simple docstring'''
import argparse
import torch
from torch import nn
from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration
def __magic_name__ ( __UpperCAmelCase ) -> str:
'''simple docstring'''
snake_case_ = [
'''encoder.version''',
... | 56 | 0 |
'''simple docstring'''
import multiprocessing
import os
from typing import BinaryIO, Optional, Union
import fsspec
from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import NestedDa... | 21 | '''simple docstring'''
import os
import sys
import warnings
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import x... | 21 | 1 |
'''simple docstring'''
from statistics import mean
import numpy as np
def lowerCamelCase (_SCREAMING_SNAKE_CASE : str , _SCREAMING_SNAKE_CASE : List[str] , _SCREAMING_SNAKE_CASE : Tuple , _SCREAMING_SNAKE_CASE : Optional[Any] ):
__a ... | 27 |
from math import factorial
class __a :
def __init__( self : Union[str, Any] , SCREAMING_SNAKE_CASE : Tuple , SCREAMING_SNAKE_CASE : Optional[int] ):
'''simple docstring'''
UpperCamelCase__ : Tuple ... | 189 | 0 |
"""simple docstring"""
import logging
import re
import pytorch_quantization
import pytorch_quantization.nn as quant_nn
import torch
from pytorch_quantization import calib
from pytorch_quantization.tensor_quant import QuantDescriptor
_lowercase : Tuple = logging.getLogger(__name__)
... | 371 |
"""simple docstring"""
from __future__ import annotations
import bisect
def lowercase__ ( snake_case_ :list[int] , snake_case_ :int , snake_case_ :int = 0 , snake_case_ :int = -1 ):
if hi < 0:
__UpperCAmelCase = len(snake_case_ )
while lo < hi:
... | 86 | 0 |
'''simple docstring'''
from __future__ import annotations
def _a( UpperCamelCase__ : list[int], UpperCamelCase__ : int, UpperCamelCase__ : int, UpperCamelCase__ : int ):
'''simple docstring'''
if (direction == 1 and array[indexa] > arra... | 152 |
'''simple docstring'''
import warnings
from pathlib import Path
from typing import List, Tuple, Union
import fire
from torch import nn
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer, PreTrainedModel
from transformers.utils import logging
a_ = logging.get_logger(__nam... | 152 | 1 |
# flake8: noqa
# Lint as: python3
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... | 141 |
from collections import OrderedDict
from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES
A_ : Union[str, Any] = logging.get_logger(__name__)
A_ : int = OrderedDict(
[
# Ba... | 141 | 1 |
from __future__ import annotations
_UpperCAmelCase : Tuple = 8.988e9 # units = N * m^s * C^-2
def A ( lowercase , lowercase , lowercase , lowercase ) -> dict[str, float]:
'''simple docstring'''
UpperCamelCase = abs(chargea * chargea )
if (fo... | 222 |
def A ( lowercase ) -> list:
'''simple docstring'''
UpperCamelCase = len(lowercase )
for i in range(1 , lowercase ):
UpperCamelCase = collection[i]
UpperCamelCase = 0
UpperCamelCase = i - 1
while low <= high:
UpperCamelCase = (low + hig... | 222 | 1 |
"""simple docstring"""
import os
import zipfile
import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links
def __snake_case ( SCREAMING_SNAKE_CASE__ : Tuple , SCREAMING_SNAKE_CASE__ : List[str]=7 ) -> Tuple:
'''simple do... | 202 |
"""simple docstring"""
from collections.abc import Callable
class UpperCAmelCase_ :
def __init__( self : Dict , A : Callable | None = None ):
# Stores actual heap items.
_UpperCAmelCase : list = []
# Stores ... | 202 | 1 |
import operator as op
lowercase : List[Any] = """scaler.pt"""
lowercase : int = """pytorch_model"""
lowercase : Union[str, Any] = """random_states"""
lowercase : Optional[Any] = """optimizer"""
lowercase : Any = ... | 99 |
import argparse
from collections import defaultdict
import yaml
lowercase : int = """docs/source/en/_toctree.yml"""
def A_ ( A__ ) -> Tuple:
a__ : int = defaultdict(A__ )
a__ : List[str] = []
a__ : Any = []
for doc in doc_list:... | 99 | 1 |
"""simple docstring"""
import enum
import shutil
import sys
UpperCAmelCase__ , UpperCAmelCase__ = shutil.get_terminal_size()
UpperCAmelCase__ = {'UP': 'A', 'DOWN': 'B', 'RIGHT': 'C', 'LEFT': 'D'}
class lowerCAmelCase__ ( enum.Enum ):
__a = 0
__a = 1
... | 354 |
"""simple docstring"""
from timeit import timeit
UpperCAmelCase__ = {
'MALAYALAM': True,
'String': False,
'rotor': True,
'level': True,
'A': True,
'BB': True,
'ABC': False,
'amanaplanacanalpanama': True, # "a man a plan a canal panama"
}
# Ensure our test d... | 40 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
UpperCamelCase = {
"configuration_mask2former": [
"MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"Mask2FormerConfig",
],
}
try:
if not is_vi... | 186 |
'''simple docstring'''
import unittest
from transformers import (
MODEL_FOR_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_CAUSAL_LM_MAPPING,
TextGenerationPipeline,
logging,
pipeline,
)
from transformers.testing_utils import (
CaptureLogger,
is_pipeline_test,
require_accelerate,
require... | 85 | 0 |
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 TestCommand
from datasets.ut... | 99 |
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():
import torch
if is_vision_availa... | 99 | 1 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_yolos import YolosImageProcessor
_lowercase : int = logging.get_logger(__name__)
class lowerCAmelCase__ ( lowerCamelCase_ ):
def __init__( self ... | 93 |
"""simple docstring"""
import argparse
from pathlib import Path
import torch
from transformers import OPTConfig, OPTModel
from transformers.utils import logging
logging.set_verbosity_info()
A = logging.get_logger(__name__)
def __A ( a_ :Tuple) -> Tu... | 160 | 0 |
'''simple docstring'''
import argparse
from typing import List
import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset
# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
from torch.optim import Ada... | 354 |
'''simple docstring'''
from collections.abc import Iterable
from typing import Any
class lowerCamelCase_ :
def __init__( self : List[Any] , _A : int | None = None ):
'''simple docstring'''
UpperCAmelCase__ : List[A... | 299 | 0 |
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
... | 219 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
_A = {'configuration_vit_mae': ['VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTMAEConfig']}
try:
if not is_torch_availab... | 62 | 0 |
"""simple docstring"""
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def _lowerCAmelCase ( ):
'''simple docstring'''
UpperCamelCase__ : List[Any] =ArgumentPar... | 371 |
"""simple docstring"""
import torch
from diffusers import StableDiffusionPipeline
_SCREAMING_SNAKE_CASE : Optional[int] = """path-to-your-trained-model"""
_SCREAMING_SNAKE_CASE : Optional[Any] = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to("""cuda""")
... | 157 | 0 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.uti... | 60 |
'''simple docstring'''
import json
import os
import pickle
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from datasets import Dataset
from transformers import is_faiss_available
from transformers.models.bart.configuration_bart import BartConfig
fr... | 56 | 0 |
import torch
from transformers import AutoModel
class __SCREAMING_SNAKE_CASE ( torch.nn.Module ):
'''simple docstring'''
def __init__( self , snake_case_="sayef/fsner-bert-base-uncased" ):
'''simple docstring'''
super(__snake_case , self ).__init_... | 358 | '''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class __SCREAMING_SNAKE_CASE ( lowerCamelCase_ ):
'''simple docstring'''
lowerCamelCase_ :Tuple = ['''image_processor''', '''tokenizer'... | 274 | 0 |
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