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 |
|---|---|---|---|---|
"""simple docstring"""
def lowerCamelCase_( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )-> bool:
# 1. Validate that path exists between current and next vertices
if graph[path[curr_ind - 1]][next_ver] == 0:
re... | 716 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase_ = {
'''configuration_clipseg''': [
'''CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''CLIPSegConfig''',
'''CLIPSegTextConfig''... | 635 | 0 |
import argparse
import shlex
import runhouse as rh
if __name__ == "__main__":
# Refer to https://runhouse-docs.readthedocs-hosted.com/en/latest/api/python/cluster.html#hardware-setup for cloud access
# setup instructions, if using on-demand hardware
# If user passes --user <user> --host <host> --key_path <... | 717 | """simple docstring"""
import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey
class _snake_case :
"""simple docstring"""
def __init__( self : int , _A : List[Any] , _A : int , _A : int):
... | 635 | 0 |
import inspect
import unittest
from transformers import YolosConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeli... | 718 | """simple docstring"""
import argparse
from collections import defaultdict
def lowerCamelCase_(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )-> int:
_SCREAMING_SNAKE_CASE : str = F"""{file}... | 635 | 0 |
"""simple docstring"""
import os
import sys
from contextlib import contextmanager
# Windows only
if os.name == "nt":
import ctypes
import msvcrt # noqa
class _snake_case ( ctypes.Structure ):
"""simple docstring"""
a = [("size", ctypes.c_int), ("visible... | 719 | """simple docstring"""
import argparse
import re
import torch
from CLAP import create_model
from transformers import AutoFeatureExtractor, ClapConfig, ClapModel
lowerCAmelCase_ = {
'''text_branch''': '''text_model''',
'''audio_branch''': '''audio_model.audio_encoder''',
'''attn''': '''attention... | 635 | 0 |
"""simple docstring"""
import math
from collections.abc import Iterator
from itertools import takewhile
def lowerCamelCase_(__SCREAMING_SNAKE_CASE )-> bool:
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number ... | 720 | """simple docstring"""
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.hugg... | 635 | 0 |
"""simple docstring"""
import hashlib
import unittest
from typing import Dict
import numpy as np
from transformers import (
MODEL_FOR_MASK_GENERATION_MAPPING,
TF_MODEL_FOR_MASK_GENERATION_MAPPING,
is_vision_available,
pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from tran... | 721 | """simple docstring"""
import argparse
import ast
import logging
import os
import sys
import pandas as pd
import torch
from tqdm import tqdm
from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as transformers_loggin... | 635 | 0 |
"""simple docstring"""
import os
from pathlib import Path
import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader
from transformers import AutoTokenizer
from transformers.models... | 700 | """simple docstring"""
import argparse
import random
import joblib
import numpy as np
import torch
from igf.igf import (
SecondaryLearner,
collect_objective_set,
compute_perplexity,
generate_datasets,
load_gpta,
recopy_gpta,
set_seed,
train_secondary_learner,
)
from torch.utils.dat... | 635 | 0 |
"""simple docstring"""
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... | 701 | """simple docstring"""
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _snake_case ( __snake_case ):
"""simple docstring"""
a = ["image_processor", "tokenizer"]
a = "ChineseCLIPImageProcessor"... | 635 | 0 |
"""simple docstring"""
def lowerCamelCase_(__SCREAMING_SNAKE_CASE = 100 )-> int:
_SCREAMING_SNAKE_CASE : Any = set()
_SCREAMING_SNAKE_CASE : List[Any] = 0
_SCREAMING_SNAKE_CASE : Tuple = n + 1 # maximum limit
for a in range(2 , __S... | 702 | """simple docstring"""
import argparse
from pathlib import Path
from typing import Dict, OrderedDict, Tuple
import torch
from audiocraft.models import MusicGen
from transformers import (
AutoFeatureExtractor,
AutoTokenizer,
EncodecModel,
MusicgenDecoderConfig,
MusicgenForConditionalGeneration... | 635 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
lowerCAmelCase_ = {
'''configuration_gpt_bigcode''': ['''GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTBigCodeConfig'''],
}
try:
if not is_torch_available():
... | 703 | """simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = {
'''asapp/sew-tiny-100k''': '''https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.js... | 635 | 0 |
"""simple docstring"""
from __future__ import annotations
import os
from typing import Any
import requests
lowerCAmelCase_ = '''https://api.github.com'''
# https://docs.github.com/en/free-pro-team@latest/rest/reference/users#get-the-authenticated-user
lowerCAmelCase_ = BASE_URL + '''/user'''
# https:/... | 704 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
lowerCAmelCase_ = {'''configuration_unispeech''': ['''UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''UniSp... | 635 | 0 |
"""simple docstring"""
import re
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class _snake_case ( __snake_case ):
"""simple docstring"""
a = ["image_processor", "tokenizer"]
a = "AutoImageProcessor"
a = ... | 705 | """simple docstring"""
import pyarrow.parquet as pq
import pytest
from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size
from ..util... | 635 | 0 |
"""simple docstring"""
def lowerCamelCase_(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )-> float:
if density <= 0:
raise ValueError("""Impossible fluid density""" )
if bulk_modulus <= 0:
raise ValueError("""Impossible bulk modulus""" )
retur... | 706 | """simple docstring"""
def lowerCamelCase_(__SCREAMING_SNAKE_CASE )-> int:
if not isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
raise TypeError("""only integers accepted as input""" )
else:
_SCREAMING_SNAKE_CASE : List[Any] = st... | 635 | 0 |
"""simple docstring"""
def lowerCamelCase_(__SCREAMING_SNAKE_CASE )-> Any:
_SCREAMING_SNAKE_CASE : Tuple = len(__SCREAMING_SNAKE_CASE )
for i in range(length - 1 ):
_SCREAMING_SNAKE_CASE : str = i
for k in range(i + 1 , __SCREAMING... | 707 | """simple docstring"""
import unittest
from queue import Empty
from threading import Thread
from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device
from ..test_modeling_common import ids_tenso... | 635 | 0 |
"""simple docstring"""
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_mvp import ... | 708 | """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-2.0... | 635 | 0 |
"""simple docstring"""
class _snake_case ( __snake_case ):
"""simple docstring"""
pass
class _snake_case ( __snake_case ):
"""simple docstring"""
pass
class _snake_case :
"""simple docstring"""
def __init__( self : ... | 709 | """simple docstring"""
import unittest
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
if is_torch_available():
import torch
from transformers import AutoModelForImageClassification
if is_vision_ava... | 635 | 0 |
"""simple docstring"""
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import (
is_pt_flax_cross_test,
require_flax,
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import is_flax_available, is_torch_availa... | 710 | """simple docstring"""
import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel
class _snake_case ( __snake_case ):
"""simple docstring"""
a = "M-CLIP"
def __init__( self : Optional[Any] , _A : List[s... | 635 | 0 |
import math
from typing import Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin
class _snake_case ( __snake_case , __snake_case ):
"""simple docstring"""
a = 1... | 711 | """simple docstring"""
from decimal import Decimal, getcontext
from math import ceil, factorial
def lowerCamelCase_(__SCREAMING_SNAKE_CASE )-> str:
if not isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
raise TypeError("""Undefined for non-integers""" )
... | 635 | 0 |
"""simple docstring"""
import unittest
from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
if is_flax... | 712 | """simple docstring"""
import argparse
from transformers import (
TapasConfig,
TapasForMaskedLM,
TapasForQuestionAnswering,
TapasForSequenceClassification,
TapasModel,
TapasTokenizer,
load_tf_weights_in_tapas,
)
from transformers.utils import logging
logging.set_verbosity_info()
d... | 635 | 0 |
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 lowerCamelCase_(__SCREAMING_SNAKE_CASE )-> int:
_SCREAMING_SNAKE_CASE : int = test_file.split(os.path.sep... | 713 | """simple docstring"""
from typing import Any
import numpy as np
def lowerCamelCase_(__SCREAMING_SNAKE_CASE )-> bool:
return np.array_equal(__SCREAMING_SNAKE_CASE , matrix.conjugate().T )
def lowerCamelCase_(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )-> Any:
_S... | 635 | 0 |
"""simple docstring"""
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
from .record_evaluation import evaluate as evaluate_record
lowerCAmelCase_ = '''\
@article{wang2019superglue,
title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},
author... | 714 | """simple docstring"""
from __future__ import annotations
def lowerCamelCase_(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , )-> tuple:
if (electron_conc, hole_conc, intrinsic_conc).count(0 ) != 1:
raise ValueError("""You cannot supply more o... | 635 | 0 |
"""simple docstring"""
import os
import random
import sys
from . import cryptomath_module as cryptomath
from . import rabin_miller
lowerCAmelCase_ = 3
def lowerCamelCase_(__SCREAMING_SNAKE_CASE )-> int:
print("""Generating primitive root of p""" )
while True:
_SCREAMI... | 715 | """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 AdamW
from torch... | 635 | 0 |
"""simple docstring"""
from math import factorial
lowerCAmelCase_ = {str(d): factorial(d) for d in range(10)}
def lowerCamelCase_( __SCREAMING_SNAKE_CASE )-> int:
return sum(DIGIT_FACTORIAL[d] for d in str(__SCREAMING_SNAKE_CASE ) )
def lowerCamelCase_( )-> int:
_SCREA... | 716 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase_ = {
'''configuration_clipseg''': [
'''CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''CLIPSegConfig''',
'''CLIPSegTextConfig''... | 635 | 0 |
import argparse
import random
import joblib
import numpy as np
import torch
from igf.igf import (
SecondaryLearner,
collect_objective_set,
compute_perplexity,
generate_datasets,
load_gpta,
recopy_gpta,
set_seed,
train_secondary_learner,
)
from torch.utils.data import DataLoader, RandomS... | 717 | """simple docstring"""
import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey
class _snake_case :
"""simple docstring"""
def __init__( self : int , _A : List[Any] , _A : int , _A : int):
... | 635 | 0 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.generation import DisjunctiveConstraint
@require_torch
class _snake_case ( unittest.TestCase ):
"""simple docstring"""
... | 718 | """simple docstring"""
import argparse
from collections import defaultdict
def lowerCamelCase_(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )-> int:
_SCREAMING_SNAKE_CASE : str = F"""{file}... | 635 | 0 |
"""simple docstring"""
from __future__ import annotations
import unittest
import numpy as np
from transformers import LayoutLMConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFMod... | 719 | """simple docstring"""
import argparse
import re
import torch
from CLAP import create_model
from transformers import AutoFeatureExtractor, ClapConfig, ClapModel
lowerCAmelCase_ = {
'''text_branch''': '''text_model''',
'''audio_branch''': '''audio_model.audio_encoder''',
'''attn''': '''attention... | 635 | 0 |
"""simple docstring"""
from decimal import Decimal, getcontext
from math import ceil, factorial
def lowerCamelCase_(__SCREAMING_SNAKE_CASE )-> str:
if not isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
raise TypeError("""Undefined for non-integers""" )
... | 720 | """simple docstring"""
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.hugg... | 635 | 0 |
"""simple docstring"""
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 _snake_case... | 721 | """simple docstring"""
import argparse
import ast
import logging
import os
import sys
import pandas as pd
import torch
from tqdm import tqdm
from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as transformers_loggin... | 635 | 0 |
from typing import Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_ima... | 636 | # Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... | 636 | 1 |
import unittest
from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
SCREAMING_SNAKE_CASE__ : int = get_tests_dir("fixtures/test_sentencepiece_with_byt... | 636 | import unittest
import numpy as np
import requests
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
... | 636 | 1 |
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import torch
from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
@dataclass
class snake_case ( UpperCamelCase_ ):
lowercase_ = 42
try:... | 636 | import heapq as hq
import math
from collections.abc import Iterator
class snake_case :
def __init__( self : str , a_ : str )-> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = str(id_ )
SCREAMING_SNAKE_CASE__ : Any =... | 636 | 1 |
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
SCREAMING_SNAKE_CASE__ : Union[str, Any] = logging.get_logger(__name__)
def _a ( lowercase__ : List[Any] ):
'''simp... | 636 | def _a ( lowercase__ : int , lowercase__ : int ):
'''simple docstring'''
return int((input_a, input_a).count(0 ) != 0 )
def _a ( ):
'''simple docstring'''
assert nand_gate(0 , 0 ) == 1
assert nand_gate(0 , 1 ) == 1
asser... | 636 | 1 |
import baseaa
def _a ( lowercase__ : str ):
'''simple docstring'''
return baseaa.baaencode(string.encode('utf-8' ) )
def _a ( lowercase__ : bytes ):
'''simple docstring'''
return baseaa.baadecode(lowercase__ ).decode('utf-8' )
if __name... | 636 | from math import factorial, radians
def _a ( lowercase__ : float , lowercase__ : int = 18 , lowercase__ : int = 10 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[Any] = angle_in_degrees - ((angle_in_degrees // 360.0) * 360.0)
# Convert... | 636 | 1 |
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class snake_case ( UpperCamelCase_ ):
@staticmethod
@abstractmethod
def __lowercase( a_ : ArgumentParser )-> Optional[Any]:
"""simple docstring"""
raise NotImplementedError()
... | 636 | import math
def _a ( lowercase__ : int ):
'''simple docstring'''
assert isinstance(lowercase__ , lowercase__ ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2 and 3 are primes
return True
elif... | 636 | 1 |
import warnings
from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum
from ...processing_utils import ProcessorMixin
if is_torch_available():
import torch
class snake_case ( UpperCamelCase_ ):
lowerca... | 636 | import inspect
import unittest
import warnings
from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
from transformers.utils import ... | 636 | 1 |
SCREAMING_SNAKE_CASE__ : Optional[int] = "\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"
SCREAMING_... | 636 | import math
import unittest
from transformers import BioGptConfig, 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 ModelTeste... | 636 | 1 |
from math import factorial, radians
def _a ( lowercase__ : float , lowercase__ : int = 18 , lowercase__ : int = 10 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[Any] = angle_in_degrees - ((angle_in_degrees // 360.0) * 360.0)
# Convert... | 636 | 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 SequenceFeatureE... | 636 | 1 |
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 TokenizerTesterMixin
SCREAMING_S... | 636 | import math
import sys
def _a ( lowercase__ : str ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[Any] = ''
try:
with open(lowercase__ , 'rb' ) as binary_file:
SCREAMING_SNAKE_CASE__ : Tuple = binary_file.read()
... | 636 | 1 |
import re
import string
from collections import Counter
import sacrebleu
import sacremoses
from packaging import version
import datasets
SCREAMING_SNAKE_CASE__ : Any = "\n@inproceedings{xu-etal-2016-optimizing,\n title = {Optimizing Statistical Machine Translation for Text Simplification},\n aut... | 636 | def _a ( lowercase__ : Optional[int] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[int] = []
SCREAMING_SNAKE_CASE__ : List[Any] = set({'(', '[', '{'} )
SCREAMING_SNAKE_CASE__ : Optional[int] = set({')', ']', '}'} )
SCREAMING_SNAKE... | 636 | 1 |
import math
import os
import unittest
from transformers import MegatronBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTeste... | 636 | 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 TokenizerTesterMixin
SCREAMING_S... | 636 | 1 |
import tempfile
import unittest
import numpy as np
from diffusers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionPipeline,
PNDMScheduler,
)
from diffusers.utils.testing_utils import is_o... | 636 | def _a ( lowercase__ : int = 1_00_00_00 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : str = [i - 1 for i in range(limit + 1 )]
for i in range(2 , limit + 1 ):
if phi[i] == i - 1:
for j in range(2 * i , limit + 1 , lowercase__ ... | 636 | 1 |
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ...tokenization_utils i... | 636 | 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 (
BertTokenizer,
ViltConfig,
ViltForImageAndTextRetrieval,
ViltForImagesAndTextClassification,
ViltForMaskedLM,
ViltForQu... | 636 | 1 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging
if TYPE_CHECKING:
from ...feature_extraction_utils import FeatureExtract... | 636 | from dataclasses import dataclass
from typing import Tuple
import numpy as np
import torch
@dataclass
class snake_case :
lowercase_ = 42 # [batch_size x 3]
lowercase_ = 42 # [batch_size x 3]
lowercase_ = 42 # [batch_size x 3]
lowercase_ = 42 # [batch_size x 3]
lowercase... | 636 | 1 |
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_MEAN,
... | 636 | import requests
SCREAMING_SNAKE_CASE__ : int = "https://newsapi.org/v1/articles?source=bbc-news&sortBy=top&apiKey="
def _a ( lowercase__ : str ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[Any] = requests.get(_NEWS_API + bbc_news_api_key ).j... | 636 | 1 |
import warnings
from typing import Dict
import numpy as np
from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQUENCE_CLASSIFICATI... | 636 | 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, RegNetYaaagf
... | 636 | 1 |
import inspect
import unittest
from typing import List
import numpy as np
from transformers import EfficientFormerConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common impor... | 636 | import warnings
from typing import List
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available
class snake_case ( UpperCamelCase_ ):
lowercase_ = ['i... | 636 | 1 |
import unittest
from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
if is_flax_available():
import o... | 636 | class snake_case ( UpperCamelCase_ ):
pass
class snake_case ( UpperCamelCase_ ):
pass
class snake_case :
def __init__( self : Union[str, Any] )-> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = ... | 636 | 1 |
from abc import ABC, abstractmethod
from typing import List, Optional
class snake_case ( UpperCamelCase_ ):
def __init__( self : Tuple )-> Optional[int]:
"""simple docstring"""
# test for the above condition
self.test()
def __lowercase( s... | 636 | from packaging import version
from .import_utils import is_accelerate_available
if is_accelerate_available():
import accelerate
def _a ( lowercase__ : List[str] ):
'''simple docstring'''
if not is_accelerate_available():
return method
SCREAMING_SNAKE_CASE... | 636 | 1 |
import inspect
import unittest
import warnings
from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
from transformers.utils import ... | 636 | 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_engine import conv... | 636 | 1 |
from __future__ import annotations
def _a ( lowercase__ : int | str ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : int = str(lowercase__ )
return n == n[::-1]
def _a ( lowercase__ : int = 1_00_00_00 ):
'''simple docstring'''
... | 636 | from __future__ import annotations
def _a ( lowercase__ : list[int | float] , lowercase__ : int , lowercase__ : int ):
'''simple docstring'''
if len(lowercase__ ) == 0:
raise ValueError('find_max() arg is an empty sequence' )
if (
left >= ... | 636 | 1 |
from __future__ import annotations
from collections.abc import Iterator
class snake_case :
def __init__( self : Union[str, Any] , a_ : int )-> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = value
SCREAMING_SNAKE_CASE__ : ... | 636 | # Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... | 636 | 1 |
def _a ( lowercase__ : int ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Any = (1 + 24 * n) ** 0.5
return ((1 + root) / 6) % 1 == 0
def _a ( lowercase__ : int = 50_00 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Tu... | 636 | import unittest
import numpy as np
import requests
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
... | 636 | 1 |
import timeit
import numpy as np
import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD
def _a ( lowercase__ : Optional[int] ):
'''simple docstring'''
def wrapper(*lowercase__ : str , **lowercase__ : List[str] )... | 636 | import heapq as hq
import math
from collections.abc import Iterator
class snake_case :
def __init__( self : str , a_ : str )-> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = str(id_ )
SCREAMING_SNAKE_CASE__ : Any =... | 636 | 1 |
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.set_grad_enabled(False)
def ... | 636 | def _a ( lowercase__ : int , lowercase__ : int ):
'''simple docstring'''
return int((input_a, input_a).count(0 ) != 0 )
def _a ( ):
'''simple docstring'''
assert nand_gate(0 , 0 ) == 1
assert nand_gate(0 , 1 ) == 1
asser... | 636 | 1 |
from typing import Optional
from torch import nn
from .transformer_ad import TransformeraDModel, TransformeraDModelOutput
class snake_case ( nn.Module ):
def __init__( self : str , a_ : int = 16 , a_ : int = 88 , a_ : Optional[int] = None , a_ : int ... | 636 | from math import factorial, radians
def _a ( lowercase__ : float , lowercase__ : int = 18 , lowercase__ : int = 10 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[Any] = angle_in_degrees - ((angle_in_degrees // 360.0) * 360.0)
# Convert... | 636 | 1 |
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
SCREAMING_SNAKE_CASE__ : Optional[Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_... | 636 | import math
def _a ( lowercase__ : int ):
'''simple docstring'''
assert isinstance(lowercase__ , lowercase__ ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2 and 3 are primes
return True
elif... | 636 | 1 |
from functools import lru_cache
@lru_cache
def _a ( lowercase__ : int ):
'''simple docstring'''
if num < 0:
raise ValueError('Number should not be negative.' )
return 1 if num in (0, 1) else num * factorial(num - 1 )
if __name__ == "__main__":
import doct... | 636 | import inspect
import unittest
import warnings
from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
from transformers.utils import ... | 636 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
SCREAMING_SNAKE_CASE__ : Optional[int] = {
"configuration_graphormer": ["GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "GraphormerConfig"],
}
try:
if no... | 636 | import math
import unittest
from transformers import BioGptConfig, 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 ModelTeste... | 636 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ : Tuple = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : Optional[Any] = {
"fac... | 636 | 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 SequenceFeatureE... | 636 | 1 |
def _a ( lowercase__ : int = 1_00_00_00 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : str = [i - 1 for i in range(limit + 1 )]
for i in range(2 , limit + 1 ):
if phi[i] == i - 1:
for j in range(2 * i , limit + 1 , lowercase__ ... | 636 | import math
import sys
def _a ( lowercase__ : str ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[Any] = ''
try:
with open(lowercase__ , 'rb' ) as binary_file:
SCREAMING_SNAKE_CASE__ : Tuple = binary_file.read()
... | 636 | 1 |
from math import ceil
def _a ( lowercase__ : int = 10_01 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Dict = 1
for i in range(1 , int(ceil(n / 2.0 ) ) ):
SCREAMING_SNAKE_CASE__ : List[Any] = 2 * i + 1
SCREAMING_SNAKE_CASE__ ... | 636 | def _a ( lowercase__ : Optional[int] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[int] = []
SCREAMING_SNAKE_CASE__ : List[Any] = set({'(', '[', '{'} )
SCREAMING_SNAKE_CASE__ : Optional[int] = set({')', ']', '}'} )
SCREAMING_SNAKE... | 636 | 1 |
def _a ( lowercase__ : Optional[int] , lowercase__ : Union[str, Any] , lowercase__ : List[Any] , lowercase__ : Optional[Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Tuple = [False] * len(lowercase__ )
SCREAMING_SNAKE_CASE__ : Dict ... | 636 | 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 TokenizerTesterMixin
SCREAMING_S... | 636 | 1 |
SCREAMING_SNAKE_CASE__ : int = "ABCDEFGHIJKLMNOPQRSTUVWXYZ"
def _a ( ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : List[Any] = input('Enter message: ' )
SCREAMING_SNAKE_CASE__ : Tuple = input('Enter key [alphanumeric]: ' )
SCREAMING_SN... | 636 | def _a ( lowercase__ : int = 1_00_00_00 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : str = [i - 1 for i in range(limit + 1 )]
for i in range(2 , limit + 1 ):
if phi[i] == i - 1:
for j in range(2 * i , limit + 1 , lowercase__ ... | 636 | 1 |
from __future__ import annotations
def _a ( lowercase__ : str , lowercase__ : list[str] | None = None ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[Any] = word_bank or []
# create a table
SCREAMING_SNAKE_CASE__ : int = len(lowerc... | 636 | 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 (
BertTokenizer,
ViltConfig,
ViltForImageAndTextRetrieval,
ViltForImagesAndTextClassification,
ViltForMaskedLM,
ViltForQu... | 636 | 1 |
import math
def _a ( lowercase__ : int ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : str = math.loga(math.sqrt(4 * positive_integer + 1 ) / 2 + 1 / 2 )
return exponent == int(lowercase__ )
def _a ( lowercase__ : float = 1 / 1_23_45 ):
... | 636 | from dataclasses import dataclass
from typing import Tuple
import numpy as np
import torch
@dataclass
class snake_case :
lowercase_ = 42 # [batch_size x 3]
lowercase_ = 42 # [batch_size x 3]
lowercase_ = 42 # [batch_size x 3]
lowercase_ = 42 # [batch_size x 3]
lowercase... | 636 | 1 |
SCREAMING_SNAKE_CASE__ : Dict = 8.314_4598
def _a ( lowercase__ : float , lowercase__ : float ):
'''simple docstring'''
if temperature < 0:
raise Exception('Temperature cannot be less than 0 K' )
if molar_mass <= 0:
raise Exception(... | 636 | import requests
SCREAMING_SNAKE_CASE__ : int = "https://newsapi.org/v1/articles?source=bbc-news&sortBy=top&apiKey="
def _a ( lowercase__ : str ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[Any] = requests.get(_NEWS_API + bbc_news_api_key ).j... | 636 | 1 |
from typing import List
from .keymap import KEYMAP, get_character
def _a ( lowercase__ : str ):
'''simple docstring'''
def decorator(lowercase__ : Tuple ):
SCREAMING_SNAKE_CASE__ : Tuple = getattr(lowercase__ , 'handle_key' , [] )
handl... | 636 | 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, RegNetYaaagf
... | 636 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
if is_sentencepiece_available():
from ..ta.tokenization_ta import TaTok... | 636 | import warnings
from typing import List
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available
class snake_case ( UpperCamelCase_ ):
lowercase_ = ['i... | 636 | 1 |
from dataclasses import asdict, dataclass
from typing import Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ : str = logging.get_logger(__name__)
# TODO Update this
SCREAMING_SNAKE_CASE__ : Tuple = {
"facebook/esm-1b": ... | 636 | class snake_case ( UpperCamelCase_ ):
pass
class snake_case ( UpperCamelCase_ ):
pass
class snake_case :
def __init__( self : Union[str, Any] )-> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = ... | 636 | 1 |
import os
import pytest
from attr import dataclass
SCREAMING_SNAKE_CASE__ : int = "us-east-1" # defaults region
@dataclass
class snake_case :
lowercase_ = 42
lowercase_ = 'arn:aws:iam::558105141721:role/sagemaker_execution_role'
lowercase_ = {
'task_name': 'mnl... | 636 | from packaging import version
from .import_utils import is_accelerate_available
if is_accelerate_available():
import accelerate
def _a ( lowercase__ : List[str] ):
'''simple docstring'''
if not is_accelerate_available():
return method
SCREAMING_SNAKE_CASE... | 636 | 1 |
import os
import tempfile
import unittest
from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property
@unittest.skipUnless(os.path.exists(UpperCamelCase_ ) , 'Tatoeba dire... | 636 | 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_engine import conv... | 636 | 1 |
def _a ( ):
'''simple docstring'''
for n in range(1 , 1_00_00_00 ):
yield n * (n + 1) // 2
def _a ( lowercase__ : Optional[int] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[int] = 1
SCREAMING_SNAKE_CASE__... | 636 | from __future__ import annotations
def _a ( lowercase__ : list[int | float] , lowercase__ : int , lowercase__ : int ):
'''simple docstring'''
if len(lowercase__ ) == 0:
raise ValueError('find_max() arg is an empty sequence' )
if (
left >= ... | 636 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE__ : Union[str, Any] = {
"configuration_trocr": ["TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP",... | 636 | # Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... | 636 | 1 |
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device
if is_torch_available():
from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments
@require_torch
class ... | 636 | import unittest
import numpy as np
import requests
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
... | 636 | 1 |
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,
MobileViTImageProcessor,
)
from transformer... | 636 | import heapq as hq
import math
from collections.abc import Iterator
class snake_case :
def __init__( self : str , a_ : str )-> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = str(id_ )
SCREAMING_SNAKE_CASE__ : Any =... | 636 | 1 |
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_tokeniza... | 636 | def _a ( lowercase__ : int , lowercase__ : int ):
'''simple docstring'''
return int((input_a, input_a).count(0 ) != 0 )
def _a ( ):
'''simple docstring'''
assert nand_gate(0 , 0 ) == 1
assert nand_gate(0 , 1 ) == 1
asser... | 636 | 1 |
import unittest
from transformers import XLMConfig, 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 ModelTesterMixin, ids_ten... | 636 | from math import factorial, radians
def _a ( lowercase__ : float , lowercase__ : int = 18 , lowercase__ : int = 10 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[Any] = angle_in_degrees - ((angle_in_degrees // 360.0) * 360.0)
# Convert... | 636 | 1 |
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, slow
f... | 636 | import math
def _a ( lowercase__ : int ):
'''simple docstring'''
assert isinstance(lowercase__ , lowercase__ ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2 and 3 are primes
return True
elif... | 636 | 1 |
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
flip_channel_order,
get_resize_output_image_size,
rescale,
resize,
to_channel_dimension_... | 636 | import inspect
import unittest
import warnings
from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
from transformers.utils import ... | 636 | 1 |
from math import sqrt
def _a ( lowercase__ : int ):
'''simple docstring'''
assert isinstance(lowercase__ , lowercase__ ) and (
number >= 0
), "'number' must been an int and positive"
SCREAMING_SNAKE_CASE__ : Optional[Any] = True
# 0 and 1 ... | 636 | import math
import unittest
from transformers import BioGptConfig, 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 ModelTeste... | 636 | 1 |
import argparse
import json
import os
import sys
import tempfile
import unittest
from argparse import Namespace
from dataclasses import dataclass, field
from enum import Enum
from pathlib import Path
from typing import List, Literal, Optional
import yaml
from transformers import HfArgumentParser, TrainingArguments
fr... | 636 | 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 SequenceFeatureE... | 636 | 1 |
def _a ( lowercase__ : int ):
'''simple docstring'''
if number > 0:
raise ValueError('input must be a negative integer' )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = len(bin(lowercase__ )[3:] )
SCREAMING_SNAKE_CASE__ : str = bin(abs(lowercase__ ) - ... | 636 | import math
import sys
def _a ( lowercase__ : str ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[Any] = ''
try:
with open(lowercase__ , 'rb' ) as binary_file:
SCREAMING_SNAKE_CASE__ : Tuple = binary_file.read()
... | 636 | 1 |
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import BatchEncoding, MarianTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import is_sentencepiece_available, is_tf_available, is_torch_available
... | 636 | def _a ( lowercase__ : Optional[int] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[int] = []
SCREAMING_SNAKE_CASE__ : List[Any] = set({'(', '[', '{'} )
SCREAMING_SNAKE_CASE__ : Optional[int] = set({')', ']', '}'} )
SCREAMING_SNAKE... | 636 | 1 |
import warnings
from ...utils import logging
from .image_processing_dpt import DPTImageProcessor
SCREAMING_SNAKE_CASE__ : Dict = logging.get_logger(__name__)
class snake_case ( UpperCamelCase_ ):
def __init__( self : Optional[int] , *a_ : int , **a_ :... | 636 | 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 TokenizerTesterMixin
SCREAMING_S... | 636 | 1 |
from sklearn.metrics import fa_score
import datasets
SCREAMING_SNAKE_CASE__ : Optional[Any] = "\nThe F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:\nF1 = 2 * (precision * recall) / (precision + recall)\n"
SCREAMING_SNAKE_CASE__ : Any = "... | 636 | def _a ( lowercase__ : int = 1_00_00_00 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : str = [i - 1 for i in range(limit + 1 )]
for i in range(2 , limit + 1 ):
if phi[i] == i - 1:
for j in range(2 * i , limit + 1 , lowercase__ ... | 636 | 1 |
def _a ( lowercase__ : str , lowercase__ : str ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : str = len(lowercase__ )
SCREAMING_SNAKE_CASE__ : Optional[int] = len(lowercase__ )
SCREAMING_SNAKE_CASE__ : Optional[int] = [[False for _ in ra... | 636 | 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 (
BertTokenizer,
ViltConfig,
ViltForImageAndTextRetrieval,
ViltForImagesAndTextClassification,
ViltForMaskedLM,
ViltForQu... | 636 | 1 |
import warnings
from diffusers import StableDiffusionImgaImgPipeline # noqa F401
warnings.warn(
"The `image_to_image.py` script is outdated. Please use directly `from diffusers import"
" StableDiffusionImg2ImgPipeline` instead."
)
| 636 | from dataclasses import dataclass
from typing import Tuple
import numpy as np
import torch
@dataclass
class snake_case :
lowercase_ = 42 # [batch_size x 3]
lowercase_ = 42 # [batch_size x 3]
lowercase_ = 42 # [batch_size x 3]
lowercase_ = 42 # [batch_size x 3]
lowercase... | 636 | 1 |
from __future__ import annotations
from collections.abc import Callable
SCREAMING_SNAKE_CASE__ : Tuple = list[list[float | int]]
def _a ( lowercase__ : Matrix , lowercase__ : Matrix ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : int = len(low... | 636 | import requests
SCREAMING_SNAKE_CASE__ : int = "https://newsapi.org/v1/articles?source=bbc-news&sortBy=top&apiKey="
def _a ( lowercase__ : str ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[Any] = requests.get(_NEWS_API + bbc_news_api_key ).j... | 636 | 1 |
import unittest
from transformers import GPTNeoXJapaneseConfig, is_torch_available
from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
fr... | 636 | 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, RegNetYaaagf
... | 636 | 1 |
import json
import sys
def _a ( lowercase__ : int , lowercase__ : Optional[int] ):
'''simple docstring'''
with open(lowercase__ , encoding='utf-8' ) as f:
SCREAMING_SNAKE_CASE__ : Any = json.load(lowercase__ )
SCREAMING_SNAKE_CASE__ : ... | 636 | import warnings
from typing import List
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available
class snake_case ( UpperCamelCase_ ):
lowercase_ = ['i... | 636 | 1 |
from __future__ import annotations
SCREAMING_SNAKE_CASE__ : List[Any] = []
def _a ( lowercase__ : list[list[int]] , lowercase__ : int , lowercase__ : int ):
'''simple docstring'''
for i in range(len(lowercase__ ) ):
if board[row][i] == 1:... | 636 | class snake_case ( UpperCamelCase_ ):
pass
class snake_case ( UpperCamelCase_ ):
pass
class snake_case :
def __init__( self : Union[str, Any] )-> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = ... | 636 | 1 |
from datetime import datetime
import requests
def _a ( lowercase__ : str ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[int] = 'https://downloadgram.net/wp-json/wppress/video-downloader/video?url='
SCREAMING_SNAKE_CASE__ : List[str] = request... | 636 | from packaging import version
from .import_utils import is_accelerate_available
if is_accelerate_available():
import accelerate
def _a ( lowercase__ : List[str] ):
'''simple docstring'''
if not is_accelerate_available():
return method
SCREAMING_SNAKE_CASE... | 636 | 1 |
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torc... | 636 | 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_engine import conv... | 636 | 1 |
from ....configuration_utils import PretrainedConfig
from ....utils import logging
SCREAMING_SNAKE_CASE__ : int = logging.get_logger(__name__)
# TODO: upload to AWS
SCREAMING_SNAKE_CASE__ : Optional[int] = {
"yjernite/retribert-base-uncased": (
"https://huggingface.co/yjernite... | 636 | from __future__ import annotations
def _a ( lowercase__ : list[int | float] , lowercase__ : int , lowercase__ : int ):
'''simple docstring'''
if len(lowercase__ ) == 0:
raise ValueError('find_max() arg is an empty sequence' )
if (
left >= ... | 636 | 1 |
from ....configuration_utils import PretrainedConfig
from ....utils import logging
SCREAMING_SNAKE_CASE__ : Tuple = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : List[Any] = {
"CarlCochet/trajectory-transformer-halfcheetah-medium-v2": (
"https://huggingface.co/CarlCoch... | 636 | # Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... | 636 | 1 |
from __future__ import annotations
def _a ( lowercase__ : list[int | float] , lowercase__ : int , lowercase__ : int ):
'''simple docstring'''
if len(lowercase__ ) == 0:
raise ValueError('find_max() arg is an empty sequence' )
if (
left >= ... | 636 | import unittest
import numpy as np
import requests
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
... | 636 | 1 |
def _a ( lowercase__ : Any , lowercase__ : Tuple ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[int] = 0
while b > 0:
if b & 1:
res += a
a += a
b >>= 1
return res
def _a ( lowercas... | 636 | import heapq as hq
import math
from collections.abc import Iterator
class snake_case :
def __init__( self : str , a_ : str )-> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = str(id_ )
SCREAMING_SNAKE_CASE__ : Any =... | 636 | 1 |
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_t... | 636 | def _a ( lowercase__ : int , lowercase__ : int ):
'''simple docstring'''
return int((input_a, input_a).count(0 ) != 0 )
def _a ( ):
'''simple docstring'''
assert nand_gate(0 , 0 ) == 1
assert nand_gate(0 , 1 ) == 1
asser... | 636 | 1 |
class snake_case :
def __init__( self : Optional[Any] , a_ : str )-> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = val
SCREAMING_SNAKE_CASE__ : Optional[Any] = None
SCREAMING_SNAKE_CASE__ : Uni... | 636 | from math import factorial, radians
def _a ( lowercase__ : float , lowercase__ : int = 18 , lowercase__ : int = 10 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[Any] = angle_in_degrees - ((angle_in_degrees // 360.0) * 360.0)
# Convert... | 636 | 1 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, ClassLabel, Features
from .base import TaskTemplate
@dataclass(frozen=UpperCamelCase_ )
class snake_case ( UpperCamelCase_ ):
lowercase_ = field(default='audio-classifi... | 636 | import math
def _a ( lowercase__ : int ):
'''simple docstring'''
assert isinstance(lowercase__ , lowercase__ ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2 and 3 are primes
return True
elif... | 636 | 1 |
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