code stringlengths 81 54k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
from __future__ import annotations
def UpperCAmelCase ( _lowerCamelCase : list[int | float] , _lowerCamelCase : int , _lowerCamelCase : int ):
'''simple docstring'''
if len(_lowerCamelCase ) == 0:
raise ValueError("find_max() ... | 709 |
import sys
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before to... | 26 | 0 |
def UpperCAmelCase ( _lowerCamelCase : int = 1_000 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Dict = -1
SCREAMING_SNAKE_CASE__ : str = 0
for a in range(1 , n // 3 ):
# Solving the two equations a**2+b**... | 710 |
from __future__ import annotations
def UpperCAmelCase ( _lowerCamelCase : list[int] , _lowerCamelCase : int ):
'''simple docstring'''
if len(_lowerCamelCase ) < k or k < 0:
raise ValueError("Invalid Input" )
SCREAMING_SNAKE_CA... | 26 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__lowercase :List[str] = {
"configuration_roberta": ["ROBERTA_PRETRAINED_CONFIG_ARCHIV... | 711 |
from __future__ import annotations
def UpperCAmelCase ( _lowerCamelCase : list[int | float] , _lowerCamelCase : int , _lowerCamelCase : int ):
'''simple docstring'''
if len(_lowerCamelCase ) == 0:
raise ValueError("find_max() ... | 26 | 0 |
def UpperCAmelCase ( _lowerCamelCase : int ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[Any] = [0] * len(_lowerCamelCase )
SCREAMING_SNAKE_CASE__ : Dict = []
SCREAMING_SNAKE_CASE__ : int = [1] * len(... | 712 |
import json
import multiprocessing
import os
import re
from collections import defaultdict
import torch
from accelerate import Accelerator
from accelerate.utils import set_seed
from arguments import HumanEvalArguments
from datasets import load_dataset, load_metric
from torch.utils.data import IterableDataset
fro... | 26 | 0 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import load_numpy, ... | 713 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__lowercase :str = {
"configuration_upernet": ["UperNetConfig"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependen... | 26 | 0 |
__lowercase :Optional[int] = 0 # The first color of the flag.
__lowercase :Union[str, Any] = 1 # The second color of the flag.
__lowercase :List[str] = 2 # The third color of the flag.
__lowercase :Optional[Any] = (red, white, blue)
def UpperCAme... | 714 |
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import datasets
import numpy as np
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
EvalPrediction,
HfArgumentParser,
PreTrainedTokenizer,
TFAutoModelForSequen... | 26 | 0 |
import os
import sys
__lowercase :int = os.path.join(os.path.dirname(__file__), "src")
sys.path.append(SRC_DIR)
from transformers import (
AutoConfig,
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForQuestionAnswering,
AutoModelForSequenceClassification,
... | 715 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
... | 26 | 0 |
'''simple docstring'''
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
__lowercase :Any = ""
__lowercase :Optional[Any] = ""
__lowercase :Tuple = ""
__lowercase :List[Any] = 1 # (0 is vertical, 1 is horizontal)
... | 716 |
import gc
import unittest
from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
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_... | 26 | 0 |
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
from .timesteps import (
fastaa_timesteps,
smartaa_timesteps,
smartaa_timesteps,
... | 717 |
# 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 having multiple reposit... | 26 | 0 |
from __future__ import annotations
import math
def UpperCAmelCase ( _lowerCamelCase : int , _lowerCamelCase : int , _lowerCamelCase : bool , _lowerCamelCase : list[int] , _lowerCamelCase : float ):
'''simple docstring'''
... | 718 |
def UpperCAmelCase ( _lowerCamelCase : int = 1_000 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Dict = -1
SCREAMING_SNAKE_CASE__ : str = 0
for a in range(1 , n // 3 ):
# Solving the two equations a**2+b**2... | 26 | 0 |
from sklearn.metrics import recall_score
import datasets
__lowercase :List[Any] = "\nRecall is the fraction of the positive examples that were correctly labeled by the model as positive. It can be computed with the equation:\nRecall = TP / (TP + FN)\nWhere TP is the true positives and FN is... | 719 |
from __future__ import annotations
def UpperCAmelCase ( _lowerCamelCase : list , _lowerCamelCase : int | None = None , _lowerCamelCase : int | None = None ):
'''simple docstring'''
if start is None:
SCREAMING_SNAKE_CASE__ : ... | 26 | 0 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowercase :List[str] = logging.get_logger(__name__)
class UpperCamelCase__ ( lowercase__ ):
"""simple docstring"""
snake_case_ = "encoder-decoder"
... | 720 |
from __future__ import annotations
from fractions import Fraction
def UpperCAmelCase ( _lowerCamelCase : int , _lowerCamelCase : int ):
'''simple docstring'''
return (
num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num ... | 26 | 0 |
'''simple docstring'''
from __future__ import annotations
class _a :
"""simple docstring"""
def __init__( self : int , a : list[list[int]] ) ->List[Any]:
SCREAMING_SNAKE_CASE__ : Optional[int] = TypeError(
"... | 721 |
import unittest
from datasets import load_dataset
from transformers.pipelines import pipeline
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_torch, slow
@is_pipeline_test
@require_torch
class _a ( unittest.TestCase ):
"""simple docstring"""
... | 26 | 0 |
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
f... | 700 |
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from huggingface_hub import HfFolder, Repository, create_repo, delete_repo
from requests.exceptions import HTTPError
import transformers
from transformers import (
CONFIG_MAPPING,
FEATUR... | 26 | 0 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by ... | 701 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _a ( lowercase__ ):
"""simple docstring"""
snake_case_ = ["image_processor", "tokenizer"]
snake_case_ = "CLIPImageProces... | 26 | 0 |
import os
from argparse import ArgumentParser, Namespace
from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand
if not is_tf_available() an... | 702 |
import sys
from collections import defaultdict
class _a :
"""simple docstring"""
def __init__( self : Any ) ->Dict:
SCREAMING_SNAKE_CASE__ : Tuple = []
def A_ ( self : int , a : List[str] ) ->Dict:
... | 26 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowercase :Optional[Any] = logging.get_logger(__name__)
__lowercase :List[str] = {
"microsoft/trocr-base-handwritten": (
"https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main... | 703 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging
if TYPE_CHECKING:
from ...onnx.config import PatchingSpec
from ...tokeniz... | 26 | 0 |
import argparse
import torch
from torch import nn
from transformers import MBartConfig, MBartForConditionalGeneration
def UpperCAmelCase ( _lowerCamelCase : Any ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : List[Any] = [
"encoder.ve... | 704 |
def UpperCAmelCase ( _lowerCamelCase : int = 4_000_000 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Any = [0, 1]
SCREAMING_SNAKE_CASE__ : List[Any] = 0
while fib[i] <= n:
fib.append(fib[i] + fib[i + 1] )
... | 26 | 0 |
import argparse
import os
import re
import packaging.version
__lowercase :int = "examples/"
__lowercase :List[Any] = {
"examples": (re.compile(R"^check_min_version\(\"[^\"]+\"\)\s*$", re.MULTILINE), "check_min_version(\"VERSION\")\n"),
"init": (re.compile(R"^__version__\s+=\s+... | 705 |
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_image... | 26 | 0 |
import numpy as np
import skfuzzy as fuzz
if __name__ == "__main__":
# Create universe of discourse in Python using linspace ()
__lowercase :List[str] = np.linspace(start=0, stop=75, num=75, endpoint=True, retstep=False)
# Create two fuzzy sets by defining any membership function
# (... | 706 |
def UpperCAmelCase ( _lowerCamelCase : int , _lowerCamelCase : bool = False ):
'''simple docstring'''
if n == 2:
return True
if not n % 2 or n < 2:
return False
if n > 5 and n % 10 not in (1, 3, 7, 9): # can quickly check ... | 26 | 0 |
import json
import os
from pathlib import Path
import pytest
from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadManager
from datasets.utils.file_utils import hash_url_to_filename
__lowercase :Any = "http://www.mocksite.com/file1.txt... | 707 |
import numpy
class _a :
"""simple docstring"""
def __init__( self : Optional[int] , a : numpy.ndarray , a : numpy.ndarray ) ->None:
SCREAMING_SNAKE_CASE__ : Any = input_array
# Random initial weights ar... | 26 | 0 |
import argparse
import torch
from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging
logging.set_verbosity_info()
def UpperCAmelCase ( _lowerCamelCase : Tuple , _lowerCamelCase : Union[str, Any] , _lower... | 708 |
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
__lowercase :Tuple = "\\n@misc{wu2016googles,\n title={Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},\n author={Yongh... | 26 | 0 |
import json
import os
import unittest
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class _a ( lowercase__ , unittest.TestCase ):
... | 709 |
import sys
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before to... | 26 | 0 |
from __future__ import annotations
from typing import Any
class _a :
"""simple docstring"""
def __init__( self : Tuple , a : int , a : int , a : float = 0 ) ->None:
SCREAMING_SNAKE_CASE__ : List[str] = ... | 710 |
from __future__ import annotations
def UpperCAmelCase ( _lowerCamelCase : list[int] , _lowerCamelCase : int ):
'''simple docstring'''
if len(_lowerCamelCase ) < k or k < 0:
raise ValueError("Invalid Input" )
SCREAMING_SNAKE_CA... | 26 | 0 |
import os
import re
import warnings
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
if TYPE_CHECKING:
from ...tokenization_utils_base import TextInput
from ...utils import loggi... | 711 |
from __future__ import annotations
def UpperCAmelCase ( _lowerCamelCase : list[int | float] , _lowerCamelCase : int , _lowerCamelCase : int ):
'''simple docstring'''
if len(_lowerCamelCase ) == 0:
raise ValueError("find_max() ... | 26 | 0 |
import inspect
import warnings
from typing import Any, Dict, Optional, Union
from packaging import version
def UpperCAmelCase ( *_lowerCamelCase : Optional[Any] , _lowerCamelCase : Optional[Union[Dict, Any]] = None , _lowerCamelCase : Dict=True , _lowerCamelCa... | 712 |
import json
import multiprocessing
import os
import re
from collections import defaultdict
import torch
from accelerate import Accelerator
from accelerate.utils import set_seed
from arguments import HumanEvalArguments
from datasets import load_dataset, load_metric
from torch.utils.data import IterableDataset
fro... | 26 | 0 |
from __future__ import annotations
def UpperCAmelCase ( _lowerCamelCase : list[int] , _lowerCamelCase : int ):
'''simple docstring'''
if len(_lowerCamelCase ) < k or k < 0:
raise ValueError("Invalid Input" )
SCREAMING_SNAKE_CA... | 713 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__lowercase :str = {
"configuration_upernet": ["UperNetConfig"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependen... | 26 | 0 |
from __future__ import annotations
from collections.abc import Iterator
class _a :
"""simple docstring"""
def __init__( self : List[Any] , a : int ) ->None:
SCREAMING_SNAKE_CASE__ : Optional[int] = value
SCREAMING_S... | 714 |
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import datasets
import numpy as np
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
EvalPrediction,
HfArgumentParser,
PreTrainedTokenizer,
TFAutoModelForSequen... | 26 | 0 |
from __future__ import annotations
import os
import tempfile
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import is_tensorflow_text_available, is_tf_available
from transformers.testing_utils import require_tensorflow_text, require_tf, slow
from ..test_modeli... | 715 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
... | 26 | 0 |
'''simple docstring'''
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
torch.se... | 716 |
import gc
import unittest
from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
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_... | 26 | 0 |
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import Callable, Dict, List, Tuple
import timm
import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams, RegNetYaagf, RegNetYaagf, RegNetY... | 717 |
# 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 having multiple reposit... | 26 | 0 |
import unittest
from transformers import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING, 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_... | 718 |
def UpperCAmelCase ( _lowerCamelCase : int = 1_000 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Dict = -1
SCREAMING_SNAKE_CASE__ : str = 0
for a in range(1 , n // 3 ):
# Solving the two equations a**2+b**2... | 26 | 0 |
def UpperCAmelCase ( _lowerCamelCase : int = 10 , _lowerCamelCase : int = 1_000 , _lowerCamelCase : bool = True ):
'''simple docstring'''
assert (
isinstance(_lowerCamelCase , _lowerCamelCase )
and isinstance(_lowerCamelC... | 719 |
from __future__ import annotations
def UpperCAmelCase ( _lowerCamelCase : list , _lowerCamelCase : int | None = None , _lowerCamelCase : int | None = None ):
'''simple docstring'''
if start is None:
SCREAMING_SNAKE_CASE__ : ... | 26 | 0 |
import numpy as np
import qiskit
def UpperCAmelCase ( _lowerCamelCase : int = 8 , _lowerCamelCase : int | None = None ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[Any] = np.random.default_rng(seed=_lowerCamelCase )
... | 720 |
from __future__ import annotations
from fractions import Fraction
def UpperCAmelCase ( _lowerCamelCase : int , _lowerCamelCase : int ):
'''simple docstring'''
return (
num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num ... | 26 | 0 |
'''simple docstring'''
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,
lo... | 721 |
import unittest
from datasets import load_dataset
from transformers.pipelines import pipeline
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_torch, slow
@is_pipeline_test
@require_torch
class _a ( unittest.TestCase ):
"""simple docstring"""
... | 26 | 0 |
def UpperCAmelCase ( _lowerCamelCase : list ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : List[Any] = 0
while len(_lowerCamelCase ) > 1:
SCREAMING_SNAKE_CASE__ : str = 0
# Consider two files with minimum ... | 700 |
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from huggingface_hub import HfFolder, Repository, create_repo, delete_repo
from requests.exceptions import HTTPError
import transformers
from transformers import (
CONFIG_MAPPING,
FEATUR... | 26 | 0 |
from .glue import glue_convert_examples_to_features, glue_output_modes, glue_processors, glue_tasks_num_labels
from .squad import SquadExample, SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
from .utils import DataProcessor, InputExample, InputFeatures, SingleSentenceClassific... | 701 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _a ( lowercase__ ):
"""simple docstring"""
snake_case_ = ["image_processor", "tokenizer"]
snake_case_ = "CLIPImageProces... | 26 | 0 |
import sys
from collections import defaultdict
class _a :
"""simple docstring"""
def __init__( self : Any ) ->Dict:
SCREAMING_SNAKE_CASE__ : Tuple = []
def A_ ( self : int , a : List[str] ) ->Dict:
... | 702 |
import sys
from collections import defaultdict
class _a :
"""simple docstring"""
def __init__( self : Any ) ->Dict:
SCREAMING_SNAKE_CASE__ : Tuple = []
def A_ ( self : int , a : List[str] ) ->Dict:
... | 26 | 0 |
from __future__ import annotations
import math
import random
from typing import Any
class _a :
"""simple docstring"""
def __init__( self : Any ) ->None:
SCREAMING_SNAKE_CASE__ : list[Any] = []
SCREAMING_SNAKE_CASE__ : int... | 703 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging
if TYPE_CHECKING:
from ...onnx.config import PatchingSpec
from ...tokeniz... | 26 | 0 |
from __future__ import annotations
__lowercase :Optional[int] = list[list[int]]
# assigning initial values to the grid
__lowercase :Matrix = [
[3, 0, 6, 5, 0, 8, 4, 0, 0],
[5, 2, 0, 0, 0, 0, 0, 0, 0],
[0, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, 8, 0],
[9, 0, ... | 704 |
def UpperCAmelCase ( _lowerCamelCase : int = 4_000_000 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Any = [0, 1]
SCREAMING_SNAKE_CASE__ : List[Any] = 0
while fib[i] <= n:
fib.append(fib[i] + fib[i + 1] )
... | 26 | 0 |
from typing import List
from .keymap import KEYMAP, get_character
def UpperCAmelCase ( _lowerCamelCase : str ):
'''simple docstring'''
def decorator(_lowerCamelCase : Dict ):
SCREAMING_SNAKE_CASE__ : Tuple = getattr(_lowerCame... | 705 |
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_image... | 26 | 0 |
import torch
from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class _a ( lowercase__ ):
"""simple docstring"""
snake_case_ = (EulerDiscreteScheduler,)
snake_case_ ... | 706 |
def UpperCAmelCase ( _lowerCamelCase : int , _lowerCamelCase : bool = False ):
'''simple docstring'''
if n == 2:
return True
if not n % 2 or n < 2:
return False
if n > 5 and n % 10 not in (1, 3, 7, 9): # can quickly check ... | 26 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__lowercase :Any = {
"configuration_deberta": ["DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP", "DebertaConfig", "Deb... | 707 |
import numpy
class _a :
"""simple docstring"""
def __init__( self : Optional[int] , a : numpy.ndarray , a : numpy.ndarray ) ->None:
SCREAMING_SNAKE_CASE__ : Any = input_array
# Random initial weights ar... | 26 | 0 |
import warnings
from ...utils import logging
from .image_processing_beit import BeitImageProcessor
__lowercase :Optional[int] = logging.get_logger(__name__)
class _a ( lowercase__ ):
"""simple docstring"""
def __init__( self : int , *a :... | 708 |
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
__lowercase :Tuple = "\\n@misc{wu2016googles,\n title={Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},\n author={Yongh... | 26 | 0 |
import argparse
import shutil
from pathlib import Path
from tqdm import tqdm
from transformers import AutoTokenizer
def UpperCAmelCase ( _lowerCamelCase : List[str] , _lowerCamelCase : Optional[Any] , _lowerCamelCase : Dict , _lowerCamelCase : Any=1_024... | 709 |
import sys
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before to... | 26 | 0 |
import unittest
from accelerate import debug_launcher
from accelerate.test_utils import require_cpu, test_ops, test_script
@require_cpu
class _a ( unittest.TestCase ):
"""simple docstring"""
def A_ ( self : List[Any] ) ->int:
debug_launch... | 710 |
from __future__ import annotations
def UpperCAmelCase ( _lowerCamelCase : list[int] , _lowerCamelCase : int ):
'''simple docstring'''
if len(_lowerCamelCase ) < k or k < 0:
raise ValueError("Invalid Input" )
SCREAMING_SNAKE_CA... | 26 | 0 |
# 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 having multiple reposit... | 711 |
from __future__ import annotations
def UpperCAmelCase ( _lowerCamelCase : list[int | float] , _lowerCamelCase : int , _lowerCamelCase : int ):
'''simple docstring'''
if len(_lowerCamelCase ) == 0:
raise ValueError("find_max() ... | 26 | 0 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
... | 712 |
import json
import multiprocessing
import os
import re
from collections import defaultdict
import torch
from accelerate import Accelerator
from accelerate.utils import set_seed
from arguments import HumanEvalArguments
from datasets import load_dataset, load_metric
from torch.utils.data import IterableDataset
fro... | 26 | 0 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional
from packaging import version
if TYPE_CHECKING:
from ... import PreTrainedTokenizer, TensorType
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from .... | 713 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__lowercase :str = {
"configuration_upernet": ["UperNetConfig"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependen... | 26 | 0 |
import importlib.metadata
import warnings
from copy import deepcopy
from packaging import version
from ..utils import logging
from .import_utils import is_accelerate_available, is_bitsandbytes_available
if is_bitsandbytes_available():
import bitsandbytes as bnb
import torch
import torch.nn a... | 714 |
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import datasets
import numpy as np
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
EvalPrediction,
HfArgumentParser,
PreTrainedTokenizer,
TFAutoModelForSequen... | 26 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__lowercase :Union[str, Any] = {
"configuration_longformer": [
"LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_... | 715 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
... | 26 | 0 |
'''simple docstring'''
import inspect
import unittest
import torch
import torch.nn as nn
from accelerate.hooks import (
AlignDevicesHook,
ModelHook,
SequentialHook,
add_hook_to_module,
attach_align_device_hook,
remove_hook_from_module,
remove_hook_from_submodules,
)
from acceler... | 716 |
import gc
import unittest
from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
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_... | 26 | 0 |
import logging
import os
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import List, Optional
import faiss
import torch
from datasets import Features, Sequence, Value, load_dataset
from transformers import DPRCo... | 717 |
# 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 having multiple reposit... | 26 | 0 |
import logging
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import librosa
import torch
from datasets import DatasetDict, load_dataset
from packaging import version
from torch import nn
from transformers import (
HfArgumentParser,
Trainer,
T... | 718 |
def UpperCAmelCase ( _lowerCamelCase : int = 1_000 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Dict = -1
SCREAMING_SNAKE_CASE__ : str = 0
for a in range(1 , n // 3 ):
# Solving the two equations a**2+b**2... | 26 | 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_com... | 719 |
from __future__ import annotations
def UpperCAmelCase ( _lowerCamelCase : list , _lowerCamelCase : int | None = None , _lowerCamelCase : int | None = None ):
'''simple docstring'''
if start is None:
SCREAMING_SNAKE_CASE__ : ... | 26 | 0 |
from __future__ import annotations
from functools import lru_cache
from math import ceil
__lowercase :Tuple = 100
__lowercase :Optional[int] = set(range(3, NUM_PRIMES, 2))
primes.add(2)
__lowercase :int
for prime in range(3, ceil(NUM_PRIMES**0.5), 2):
if prime not in primes... | 720 |
from __future__ import annotations
from fractions import Fraction
def UpperCAmelCase ( _lowerCamelCase : int , _lowerCamelCase : int ):
'''simple docstring'''
return (
num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num ... | 26 | 0 |
'''simple docstring'''
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
__lowercase :Tuple = "\\n@misc{wu2016googles,\n title={Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Transl... | 721 |
import unittest
from datasets import load_dataset
from transformers.pipelines import pipeline
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_torch, slow
@is_pipeline_test
@require_torch
class _a ( unittest.TestCase ):
"""simple docstring"""
... | 26 | 0 |
import argparse
import os
# New Code #
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... | 700 |
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from huggingface_hub import HfFolder, Repository, create_repo, delete_repo
from requests.exceptions import HTTPError
import transformers
from transformers import (
CONFIG_MAPPING,
FEATUR... | 26 | 0 |
def UpperCAmelCase ( _lowerCamelCase : int ):
'''simple docstring'''
if not isinstance(_lowerCamelCase , _lowerCamelCase ):
raise TypeError("only integers accepted as input" )
else:
SCREAMING_SNAKE_CASE__ : List[str] = ... | 701 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _a ( lowercase__ ):
"""simple docstring"""
snake_case_ = ["image_processor", "tokenizer"]
snake_case_ = "CLIPImageProces... | 26 | 0 |
import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset
from ..utils.generic import ModelOutput
class _a ( lowercase__ ):
"""simple docstring"""
def __init__( self : Any , a : List[str] , a : List[str] ... | 702 |
import sys
from collections import defaultdict
class _a :
"""simple docstring"""
def __init__( self : Any ) ->Dict:
SCREAMING_SNAKE_CASE__ : Tuple = []
def A_ ( self : int , a : List[str] ) ->Dict:
... | 26 | 0 |
import torch
def UpperCAmelCase ( ):
'''simple docstring'''
if torch.cuda.is_available():
SCREAMING_SNAKE_CASE__ : str = torch.cuda.device_count()
else:
SCREAMING_SNAKE_CASE__ : str = 0
print(f"""Successfully r... | 703 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging
if TYPE_CHECKING:
from ...onnx.config import PatchingSpec
from ...tokeniz... | 26 | 0 |
import argparse
import torch
from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
__lowercase :Any = logging.get_logger(__name__)
__lowercase :List[Any] = [
["attention", "attn"],
["... | 704 |
def UpperCAmelCase ( _lowerCamelCase : int = 4_000_000 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Any = [0, 1]
SCREAMING_SNAKE_CASE__ : List[Any] = 0
while fib[i] <= n:
fib.append(fib[i] + fib[i + 1] )
... | 26 | 0 |
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
__lowercase :Union[str, Any] = 200
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generati... | 705 |
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_image... | 26 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__lowercase :Tuple = {"configuration_fnet": ["FNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "FNetConfig"]}
try:... | 706 |
def UpperCAmelCase ( _lowerCamelCase : int , _lowerCamelCase : bool = False ):
'''simple docstring'''
if n == 2:
return True
if not n % 2 or n < 2:
return False
if n > 5 and n % 10 not in (1, 3, 7, 9): # can quickly check ... | 26 | 0 |
def UpperCAmelCase ( _lowerCamelCase : int , _lowerCamelCase : int , _lowerCamelCase : list[list[int]] ):
'''simple docstring'''
def update_area_of_max_square(_lowerCamelCase : int , _lowerCamelCase : int ) -> int:
# ... | 707 |
import numpy
class _a :
"""simple docstring"""
def __init__( self : Optional[int] , a : numpy.ndarray , a : numpy.ndarray ) ->None:
SCREAMING_SNAKE_CASE__ : Any = input_array
# Random initial weights ar... | 26 | 0 |
from typing import Dict, Iterable, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
IMAGENET_STANDARD_MEA... | 708 |
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
__lowercase :Tuple = "\\n@misc{wu2016googles,\n title={Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},\n author={Yongh... | 26 | 0 |
from random import shuffle
import tensorflow as tf
from numpy import array
def UpperCAmelCase ( _lowerCamelCase : Tuple , _lowerCamelCase : Dict ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[Any] = int(_lowerCamelCase )
... | 709 |
import sys
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before to... | 26 | 0 |
from __future__ import annotations
from cmath import sqrt
def UpperCAmelCase ( _lowerCamelCase : int , _lowerCamelCase : int , _lowerCamelCase : int ):
'''simple docstring'''
if a == 0:
raise ValueError("Coefficient 'a' must not... | 710 |
from __future__ import annotations
def UpperCAmelCase ( _lowerCamelCase : list[int] , _lowerCamelCase : int ):
'''simple docstring'''
if len(_lowerCamelCase ) < k or k < 0:
raise ValueError("Invalid Input" )
SCREAMING_SNAKE_CA... | 26 | 0 |
from statistics import mean, stdev
def UpperCAmelCase ( _lowerCamelCase : list , _lowerCamelCase : int = 3 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Tuple = min(_lowerCamelCase )
SCREAMING_SNAKE_CASE__ : int = ... | 711 |
from __future__ import annotations
def UpperCAmelCase ( _lowerCamelCase : list[int | float] , _lowerCamelCase : int , _lowerCamelCase : int ):
'''simple docstring'''
if len(_lowerCamelCase ) == 0:
raise ValueError("find_max() ... | 26 | 0 |
from ...utils import is_torch_available, is_transformers_available
if is_transformers_available() and is_torch_available():
from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline | 712 |
import json
import multiprocessing
import os
import re
from collections import defaultdict
import torch
from accelerate import Accelerator
from accelerate.utils import set_seed
from arguments import HumanEvalArguments
from datasets import load_dataset, load_metric
from torch.utils.data import IterableDataset
fro... | 26 | 0 |
import warnings
from ...utils import logging
from .image_processing_owlvit import OwlViTImageProcessor
__lowercase = logging.get_logger(__name__)
class _a ( lowercase__ ):
"""simple docstring"""
def __init__( self : str , *a : Optional[Any... | 713 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__lowercase :str = {
"configuration_upernet": ["UperNetConfig"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependen... | 26 | 0 |
from __future__ import annotations
from fractions import Fraction
def UpperCAmelCase ( _lowerCamelCase : int , _lowerCamelCase : int ):
'''simple docstring'''
return (
num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num ... | 714 |
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import datasets
import numpy as np
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
EvalPrediction,
HfArgumentParser,
PreTrainedTokenizer,
TFAutoModelForSequen... | 26 | 0 |
from random import randint
from tempfile import TemporaryFile
import numpy as np
def UpperCAmelCase ( _lowerCamelCase : Optional[int] , _lowerCamelCase : int , _lowerCamelCase : Union[str, Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_... | 715 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
... | 26 | 0 |
'''simple docstring'''
import argparse
import json
import os
import torch
from torch import nn
from transformers import NllbMoeConfig, NllbMoeModel
from transformers.modeling_utils import dtype_byte_size
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
def UpperCAmelCase (... | 716 |
import gc
import unittest
from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
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_... | 26 | 0 |
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import SPIECE_UNDERLINE, is_sentencepiece_available
from transformers.models.speech_to_text import SpeechaTextTokenizer
from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_FILES_NAMES, save_json
fr... | 717 |
# 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 having multiple reposit... | 26 | 0 |
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_image... | 718 |
def UpperCAmelCase ( _lowerCamelCase : int = 1_000 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Dict = -1
SCREAMING_SNAKE_CASE__ : str = 0
for a in range(1 , n // 3 ):
# Solving the two equations a**2+b**2... | 26 | 0 |
from __future__ import annotations
def UpperCAmelCase ( _lowerCamelCase : Any , _lowerCamelCase : Any , _lowerCamelCase : int , _lowerCamelCase : Union[str, Any] ): # noqa: E741
'''simple docstring'''
while r - l > 1:
S... | 719 |
from __future__ import annotations
def UpperCAmelCase ( _lowerCamelCase : list , _lowerCamelCase : int | None = None , _lowerCamelCase : int | None = None ):
'''simple docstring'''
if start is None:
SCREAMING_SNAKE_CASE__ : ... | 26 | 0 |
def UpperCAmelCase ( _lowerCamelCase : int ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : int = (1 + 24 * n) ** 0.5
return ((1 + root) / 6) % 1 == 0
def UpperCAmelCase ( _lowerCamelCase : int = 5_000 ):
... | 720 |
from __future__ import annotations
from fractions import Fraction
def UpperCAmelCase ( _lowerCamelCase : int , _lowerCamelCase : int ):
'''simple docstring'''
return (
num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num ... | 26 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ..utils import _LazyModule
__lowercase :Optional[int] = {
"config": [
"EXTERNAL_DATA_FORMAT_SIZE_LIMIT",
"OnnxConfig",
"OnnxConfigWithPast",
"OnnxSeq2SeqConfigWithPast",
"PatchingSpec",
... | 721 |
import unittest
from datasets import load_dataset
from transformers.pipelines import pipeline
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_torch, slow
@is_pipeline_test
@require_torch
class _a ( unittest.TestCase ):
"""simple docstring"""
... | 26 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__magic_name__ = {'configuration_wavlm': ['WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'WavLMConfig']}
try:
if not is_torch_available():
raise OptionalDep... | 27 |
'''simple docstring'''
import argparse
import json
from typing import List
from ltp import LTP
from transformers.models.bert.tokenization_bert import BertTokenizer
def lowerCamelCase ( lowerCamelCase : Optional[Any]):
# This defines a "chinese character" as anything in the CJK Unico... | 27 | 1 |
'''simple docstring'''
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
... | 27 |
'''simple docstring'''
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class __lowerCAmelCase ( ... | 27 | 1 |
'''simple docstring'''
def lowerCamelCase ( lowerCamelCase : int):
if n == 1 or not isinstance(lowerCamelCase , lowerCamelCase):
return 0
elif n == 2:
return 1
else:
A_ : List[str] = [0, 1]
for i in r... | 27 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class __lowerCAmelCase ( metaclass=__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
a_ = ["""torch""", """torchsde"""]
def __init__( self : Any ,*_a : Union[str, Any] ,**_a : ... | 27 | 1 |
'''simple docstring'''
def lowerCamelCase ( lowerCamelCase : str):
A_ : int = 0
# if input_string is "aba" than new_input_string become "a|b|a"
A_ : Dict = """"""
A_ : Any = """"""
# append each character + "|... | 27 |
'''simple docstring'''
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data ... | 27 | 1 |
'''simple docstring'''
def lowerCamelCase ( lowerCamelCase : List[str] , lowerCamelCase : Optional[int]):
A_ : Union[str, Any] = [1]
for i in range(2 , lowerCamelCase):
factorials.append(factorials[-1] * i)
assert 0 <= ... | 27 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__magic_name__ = {}
try:
if not is_sentencepiece_available():
raise Op... | 27 | 1 |
'''simple docstring'''
import gc
import unittest
from diffusers import FlaxStableDiffusionInpaintPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax... | 27 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from diffusers import (
DDIMScheduler,
KandinskyVaaControlnetPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, ... | 27 | 1 |
'''simple docstring'''
from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder
# @@protoc_insertion_point(imports)
__magic_name__... | 27 |
'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, PreTrainedToke... | 27 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, PreTrainedToke... | 27 |
'''simple docstring'''
import sys
import webbrowser
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
print('Googling.....')
__magic_name__ = 'https://www.google.com/search?q=' + ' '.join(sys.argv[1:])
__magic_name__ = reque... | 27 | 1 |
'''simple docstring'''
import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig
if TYPE_CHECKIN... | 27 |
'''simple docstring'''
from ... import PretrainedConfig
__magic_name__ = {
'sijunhe/nezha-cn-base': 'https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json',
}
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
a_ = NEZHA_PRE... | 27 | 1 |
'''simple docstring'''
def lowerCamelCase ( lowerCamelCase : list , lowerCamelCase : int , lowerCamelCase : int = 0 , lowerCamelCase : int = 0):
A_ : str = right or len(lowerCamelCase) - 1
if left > right:
... | 27 |
'''simple docstring'''
from __future__ import annotations
def lowerCamelCase ( lowerCamelCase : dict , lowerCamelCase : str):
A_ , A_ : List[Any] = set(lowerCamelCase), [start]
while stack:
A_ : Optional[Any] =... | 27 | 1 |
'''simple docstring'''
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from trans... | 27 |
'''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 (
MobileViTConfig,
MobileViTForImageClassification,
MobileViTForSemanticSegmentation,
MobileViTImageP... | 27 | 1 |
'''simple docstring'''
import os
def lowerCamelCase ( ):
with open(os.path.dirname(lowerCamelCase) + """/grid.txt""") as f:
A_ : str = [] # noqa: E741
for _ in range(20):
l.append([int(lowerCamelCase) for x in f.readline().split()... | 27 |
'''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,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
r... | 27 | 1 |
'''simple docstring'''
import sys
__magic_name__ = (
'73167176531330624919225119674426574742355349194934'
'96983520312774506326239578318016984801869478851843'
'85861560789112949495459501737958331952853208805511'
'12540698747158523863050715693290963295227443043557'
'668966489504452445231... | 27 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_owlvit import OwlViTImageProcessor
__magic_name__ = logging.get_logger(__name__)
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__( self : ... | 27 | 1 |
'''simple docstring'''
import datasets
from .evaluate import evaluate
__magic_name__ = '\\n@inproceedings{Rajpurkar2016SQuAD10,\n title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},\n author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},\n booktitle={EMNLP}... | 27 |
'''simple docstring'''
from sympy import diff, lambdify, symbols
from sympy.functions import * # noqa: F403
def lowerCamelCase ( lowerCamelCase : str , lowerCamelCase : complex , lowerCamelCase : str = "x" , lowerCamelCase : float = 10**-1... | 27 | 1 |
'''simple docstring'''
import qiskit
def lowerCamelCase ( lowerCamelCase : int , lowerCamelCase : int):
A_ : Tuple = qiskit.Aer.get_backend("""aer_simulator""")
# Create a Quantum Circuit acting on the q register
A_ : Optional[int]... | 27 |
'''simple docstring'''
import json
import os
from collections import Counter
import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset
__magic_name__ = {1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1),... | 27 | 1 |
'''simple docstring'''
# Copyright 2022 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
#
# ... | 27 |
'''simple docstring'''
from __future__ import annotations
import math
def lowerCamelCase ( lowerCamelCase : int):
if num <= 0:
A_ : List[Any] = F'{num}: Invalid input, please enter a positive integer.'
raise ValueError(lowerCamelCase)
... | 27 | 1 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, XLMRobertaTokenizer
from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.alt_diffusion.modelin... | 27 |
'''simple docstring'''
import argparse
import logging
import os
import time
import timeit
import datasets
import numpy as np
import pycuda.autoinit # noqa: F401
import pycuda.driver as cuda
import tensorrt as trt
import torch
from absl import logging as absl_logging
from accelerate import Accelerator
from datase... | 27 | 1 |
'''simple docstring'''
import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def lowerCamelCase ... | 27 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
__magic_name__ = {
'configuration_convnext': ['CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ConvNext... | 27 | 1 |
'''simple docstring'''
import argparse
import json
from typing import List
from ltp import LTP
from transformers.models.bert.tokenization_bert import BertTokenizer
def lowerCamelCase ( lowerCamelCase : Optional[Any]):
# This defines a "chinese character" as anything in the CJK Unico... | 27 |
'''simple docstring'''
import copy
import os
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Dict, Mapping, Optional, Union
if TYPE_CHECKING:
from ...processing_utils import ProcessorMixin
from ...utils import TensorType
from ...configuration_utils import PretrainedConfig
fr... | 27 | 1 |
'''simple docstring'''
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_available, ... | 27 |
'''simple docstring'''
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
import torch
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFe... | 27 | 1 |
'''simple docstring'''
from __future__ import annotations
class __lowerCAmelCase :
'''simple docstring'''
def __init__( self : Union[str, Any] ,_a : Any=None ):
'''simple docstring'''
A_ : List[Any] = data
A_ : Optional[int] ... | 27 |
'''simple docstring'''
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTes... | 27 | 1 |
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