code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
'''simple docstring'''
import json
import os
import unittest
from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.uti... | 4 |
"""simple docstring"""
def __lowerCAmelCase ( lowercase : int ) -> bool:
"""simple docstring"""
if p < 2:
raise ValueError("p should not be less than 2!" )
elif p == 2:
return True
snake_case : Dict = 4
snake_case : str ... | 203 | 0 |
from pathlib import Path
from typing import List
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText
if is_torch_available():
... | 113 |
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required ... | 113 | 1 |
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
snake_case_ : Union[str, Any] = {"configuration_gpt_neox": ["GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoXConfig"]}
... | 51 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case_ : int = logging.get_logger(__name__)
snake_case_ : str = {}
class __snake_case ( a ):
UpperCAmelCase__ : str = '''llama'''
UpperCAmelCase__ : ... | 51 | 1 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaImgaImgPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.ut... | 128 |
"""simple docstring"""
def a_ ( _lowercase = 100 ):
_UpperCamelCase : List[Any] = 0
_UpperCamelCase : Optional[Any] = 0
for i in range(1 , n + 1 ):
sum_of_squares += i**2
sum_of_ints += i
... | 128 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
a__ : List[Any] = logging.get_logger(__name__)
a__ : List[Any] = {
'''google/bit-50''': '''https://hu... | 313 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a__ : Optional[Any] = logging.get_logger(__name__)
a__ : List[str] = {
'''kssteven/ibert-roberta-base''': ... | 313 | 1 |
"""simple docstring"""
import argparse
from pathlib import Path
import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version
from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging
if ... | 356 |
"""simple docstring"""
def __a ( __lowerCamelCase, __lowerCamelCase, __lowerCamelCase, __lowerCamelCase ):
# Return True if there is node that has not iterated.
UpperCAmelCase_ : List[Any] = [False] * len(__lowerCamelCase )
UpperCAmelCase_ : Any = []
queue.append... | 23 | 0 |
import numpy as np
import torch
from imwatermark import WatermarkEncoder
# Copied from https://github.com/Stability-AI/generative-models/blob/613af104c6b85184091d42d374fef420eddb356d/scripts/demo/streamlit_helpers.py#L66
UpperCAmelCase_ : Any = 0B1011_0011_1110_1100_1001_0000_0111_1011_1011_0001... | 38 | '''simple docstring'''
import os
def lowerCamelCase ( UpperCAmelCase__ : str = "input.txt" ) -> int:
with open(os.path.join(os.path.dirname(UpperCAmelCase__ ) , UpperCAmelCase__ ) ) as input_file:
lowercase_ : str = [
... | 239 | 0 |
'''simple docstring'''
import os
import unittest
from transformers.models.cpmant.tokenization_cpmant import VOCAB_FILES_NAMES, CpmAntTokenizer
from transformers.testing_utils import require_jieba, tooslow
from ...test_tokenization_common import TokenizerTesterMixin
@require_jieba
class a_ ( _lowe... | 359 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
lowerCAmelCase : int ... | 147 | 0 |
'''simple docstring'''
from __future__ import annotations
SCREAMING_SNAKE_CASE_: Any =[]
def lowerCAmelCase_ ( snake_case_ : list[list[int]] , snake_case_ : int , snake_case_ : int ) -> bool:
'''simple docstring'''
for i in range(len(snak... | 1 | '''simple docstring'''
from typing import Dict
import numpy as np
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline, PipelineException
if is_tf_available():
import tensorflow as tf
from ..tf_utils import s... | 1 | 1 |
"""simple docstring"""
from typing import Callable, List, Optional, Union
import PIL
import torch
from transformers import (
CLIPImageProcessor,
CLIPSegForImageSegmentation,
CLIPSegProcessor,
CLIPTextModel,
CLIPTokenizer,
)
from diffusers import DiffusionPipeline
from diffusers.configurati... | 360 |
"""simple docstring"""
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
_lowerCAmelCase : Optional[int] = logging.get_logger(__name__... | 202 | 0 |
'''simple docstring'''
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 HfArgu... | 211 |
'''simple docstring'''
import unicodedata
from dataclasses import dataclass
from typing import Optional, Union
import numpy as np
from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokeni... | 211 | 1 |
"""simple docstring"""
from __future__ import annotations
from typing import Any
def SCREAMING_SNAKE_CASE_ ( snake_case : list[Any] )-> None:
create_state_space_tree(snake_case , [] , 0 )
def SCREAMING_SNAKE_CASE_ ( snake_case : list[Any] , snake_case ... | 367 |
"""simple docstring"""
def SCREAMING_SNAKE_CASE_ ( snake_case : list )-> list:
def merge(snake_case : list , snake_case : list ) -> list:
def _merge():
while left and right:
yield (left if left[0] <= right[0... | 80 | 0 |
'''simple docstring'''
from pickle import UnpicklingError
import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict
from ..utils import logging
_lowerCAmelCase = logging.get_logger(__name__)
def _SCREAMING_SNAKE... | 37 |
'''simple docstring'''
from __future__ import annotations
import math
from collections import Counter
from string import ascii_lowercase
def _SCREAMING_SNAKE_CASE ( UpperCamelCase ):
"""simple docstring"""
lowerCAmelCase__ , lowerCAmelCase__ : List[str] = an... | 37 | 1 |
"""simple docstring"""
import os
from pathlib import Path
def lowercase_ ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ) -> str:
"""simple docstring"""
lowerCAmelCase__ : List[str] = {
"""en""": """Machine learning is great, isn't it... | 360 |
"""simple docstring"""
from __future__ import annotations
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import is_tf_available, is_vision_available
from ...test_modeling_tf_common import fl... | 212 | 0 |
import unittest
from transformers import AutoTokenizer, FalconConfig, 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 Model... | 275 |
import unittest
from transformers import DebertaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from .... | 275 | 1 |
def a ( SCREAMING_SNAKE_CASE_ : Optional[Any] , SCREAMING_SNAKE_CASE_ : List[Any] , SCREAMING_SNAKE_CASE_ : Optional[Any] , SCREAMING_SNAKE_CASE_ : Optional[int] ):
global f # a global dp table for knapsack
if f[i][j] < 0:
... | 355 |
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel
from ...utils import logging
__UpperCAmelCase : str = logging.get_logger(__name__)
def a ( SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : Optional... | 315 | 0 |
'''simple docstring'''
class _a :
'''simple docstring'''
def __init__( self ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Any = 0
SCREAMING_SNAKE_CASE : str = 0
... | 251 |
'''simple docstring'''
class _a :
'''simple docstring'''
def __init__( self ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Any = 0
SCREAMING_SNAKE_CASE : str = 0
... | 251 | 1 |
import inspect
import unittest
from transformers import ConvNextConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import BackboneTesterMixin
fr... | 308 |
from __future__ import annotations
import matplotlib.pyplot as plt # type: ignore
import numpy
# initial triangle of Koch snowflake
_lowerCAmelCase : Optional[Any] = numpy.array([0, 0])
_lowerCAmelCase : Dict = numpy.array([0.5, 0.8660254])
_lowerCAmelCase : Any = ... | 308 | 1 |
'''simple docstring'''
import unittest
from transformers import load_tool
from transformers.utils import is_torch_available
if is_torch_available():
import torch
from transformers.testing_utils import require_torch
from .test_tools_common import ToolTesterMixin
@require_torch
class ... | 28 |
'''simple docstring'''
import io
import json
import fsspec
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.json import JsonDatasetReader, JsonDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_... | 23 | 0 |
from pathlib import Path
import fire
from tqdm import tqdm
def A_ ( _lowerCAmelCase="ro" , _lowerCAmelCase="en" , _lowerCAmelCase="wmt16" , _lowerCAmelCase=None ) -> List[str]:
try:
import datasets
except (ModuleNotFoundError, ImportError):
ra... | 365 |
from scipy.stats import spearmanr
import datasets
__lowerCamelCase : List[str] = """
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no correlation.
Positive... | 140 | 0 |
'''simple docstring'''
from argparse import ArgumentParser, Namespace
from ..utils import logging
from . import BaseTransformersCLICommand
def UpperCamelCase_ ( snake_case_ : Namespace ) -> Any:
'''simple docstring'''
return ConvertCommand(
... | 229 | '''simple docstring'''
def UpperCamelCase_ ( snake_case_ : int , snake_case_ : int ) -> str:
'''simple docstring'''
if a < 0 or b < 0:
raise ValueError("""the value of both inputs must be positive""" )
__lowerCAmelCase =... | 229 | 1 |
def __lowercase ( _UpperCamelCase, _UpperCamelCase ) ->list[str]:
"""simple docstring"""
return [sentence[i : i + ngram_size] for i in range(len(_UpperCamelCase ) - ngram_size + 1 )]
if __name__ == "__main__":
from doctest import testmod
testmod()
| 173 |
# Note: if you intend to run this script make sure you look under scripts/fsmt/
# to locate the appropriate script to do the work correctly. There is a set of scripts to:
# - download and prepare data and run the conversion script
# - perform eval to get the best hparam into the config
# - generate model_cards - use... | 173 | 1 |
'''simple docstring'''
def __UpperCAmelCase ( a_: Tuple, a_: List[str], a_: List[str] ):
if n == 0:
return 1
elif n % 2 == 1:
return (binary_exponentiation(a_, n - 1, a_ ) * a) % mod
else:
_UpperCAmelCase : Any = binary... | 145 | '''simple docstring'''
def __UpperCAmelCase ( a_: str, a_: str ):
if len(a_ ) != len(a_ ):
raise ValueError("String lengths must match!" )
_UpperCAmelCase : Dict = 0
for chara, chara in zip(a_, a_ ):
if chara != chara:
... | 145 | 1 |
"""simple docstring"""
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def _lowerCAmelCase ( ):
__SCREAMING_SNAKE_CASE = HfArgumentParser(UpperCamelCase_ )
__SCREAMING_SNAKE_CASE = parser.parse_args_into_dataclasses()[0]
__SCREAMIN... | 354 |
"""simple docstring"""
def _lowerCAmelCase ( UpperCamelCase_ , UpperCamelCase_ ):
while b:
__SCREAMING_SNAKE_CASE ,__SCREAMING_SNAKE_CASE = b, a % b
return a
def _lowerCAmelCase ( UpperCamelCase_ , UpperCamelCase_ ):
return a if b == 0 else euclidean_gcd_r... | 255 | 0 |
"""simple docstring"""
from __future__ import annotations
def lowercase ( _SCREAMING_SNAKE_CASE : list , _SCREAMING_SNAKE_CASE : int | None = None , _SCREAMING_SNAKE_CASE : int | None = None ):
'''simple docstring'''
if star... | 260 |
"""simple docstring"""
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
__A : Union[str, Any] = "\\n\n"
__A : Any = "\nPerplexity... | 260 | 1 |
'''simple docstring'''
import logging
import numpy as np
import pytest
from scipy.linalg import eigh
logging.basicConfig(level=logging.INFO, format="%(message)s")
def SCREAMING_SNAKE_CASE__ ( __A ) -> np.ndarray:
return input_array.reshape((input_array.size, 1) )
def ... | 160 |
'''simple docstring'''
class __UpperCAmelCase :
def __init__( self , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ):
"""simple docstring"""
_snake_case = name
_snake_case = value
_snake_case ... | 160 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
lowerCAmelCase_ = {
'''configuration_falcon''': ['''FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FalconConfig'''],
}
try:
if not is_torch_available():
raise Option... | 279 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torc... | 279 | 1 |
import cva
import numpy as np
class UpperCamelCase__ :
'''simple docstring'''
def __init__( self : str ,lowerCamelCase__ : str ,lowerCamelCase__ : int ) -> Optional[Any]:
'''simple docstring'''
if k in ... | 353 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE_ = {"""configuration_reformer""": ["""REFORMER_PRETRAINED_CONFIG_... | 193 | 0 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorf... | 70 |
from typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, FlaxT... | 313 | 0 |
def __lowerCamelCase ( lowerCamelCase__ : int , lowerCamelCase__ : int ):
'''simple docstring'''
if a < 0 or b < 0:
raise ValueError("""the value of both inputs must be positive""" )
lowerCamelCase = str(bin(lowerCamelCase__ ) )[2:] ... | 66 |
def __lowerCamelCase ( ):
'''simple docstring'''
lowerCamelCase = []
lowerCamelCase = 1
while len(lowerCamelCase__ ) < 1E6:
constant.append(str(lowerCamelCase__ ) )
i += 1
lowerCamelCase = """""".join(lowerCamelCase__ ... | 66 | 1 |
'''simple docstring'''
from datetime import datetime
import requests
def _A ( A__ ):
"""simple docstring"""
__lowercase = '''https://downloadgram.net/wp-json/wppress/video-downloader/video?url='''
__lowercase = requests.get(base_url + url ).json()[0]['''urls''']... | 104 |
"""simple docstring"""
_UpperCamelCase: Dict = [
9_9_9,
8_0_0,
7_9_9,
6_0_0,
5_9_9,
5_0_0,
4_0_0,
3_9_9,
3_7_7,
3_5_5,
3_3_3,
3_1_1,
2_8_8,
2_6_6,
2_4_4,
2_2_2,
2_0_0,
1_9_9,
1_7_7,
1_5_5,
... | 255 | 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 ap... | 369 |
# 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 ap... | 189 | 0 |
'''simple docstring'''
def lowercase__ ( __lowercase : Union[str, Any] , __lowercase : List[Any] , __lowercase : Optional[int] ) -> Any:
"""simple docstring"""
if n == 0:
return 1
elif n % 2 == 1:
return (binary_... | 53 |
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
__snake_case :Optional[int] = 200
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generati... | 49 | 0 |
import itertools
import random
import unittest
import numpy as np
from transformers import ASTFeatureExtractor
from transformers.testing_utils import require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import S... | 370 |
"""simple docstring"""
def _A ( _a : int ):
"""simple docstring"""
A = abs(_a )
A = 0
while n > 0:
res += n % 1_0
n //= 1_0
return res
def _A ( _a... | 77 | 0 |
'''simple docstring'''
from collections import Counter
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
lowercase__ : Tuple = datasets.load_iris()
lowercase__ : Union[str, Any] = np.array(data['data'])
lowercase__ : Lis... | 324 |
"""simple docstring"""
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
UNetaDConditionModel,
VideoToVideoSDPipeline,
)
from diffusers.utils import floats... | 153 | 0 |
"""simple docstring"""
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from accelerate import... | 244 |
"""simple docstring"""
import math
def a__ ( _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
UpperCamelCase = 0
UpperCamelCase = 0
while num > 0:
UpperCamelCase = num % 8
UpperCamelCase = octal + (remainder * math.floor(math.pow(10 , _SCREAMING_SNAKE_CAS... | 244 | 1 |
"""simple docstring"""
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
__UpperCamelCase = logging.get_logger(__name__)
__UpperCamelCase ... | 69 | """simple docstring"""
def UpperCAmelCase ( UpperCAmelCase , UpperCAmelCase ) -> int:
while a != 0:
snake_case_ , snake_case_ = b % a, a
return b
def UpperCAmelCase ( UpperCAmelCase , UpperCAmelCase ) -> int:
... | 69 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__lowercase = {
"""configuration_instructblip""": [
"""INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""InstructBl... | 359 |
"""simple docstring"""
import copy
import fnmatch
import json
import os
import pickle as pkl
import shutil
import sys
import tarfile
import tempfile
from collections import OrderedDict
from contextlib import contextmanager
from functools import partial
from hashlib import shaaaa
from i... | 226 | 0 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE_ ( _UpperCAmelCase : str ) -> list[int]:
_a : Union[str, Any] =[0 for i in range(len(_UpperCAmelCase ) )]
# initialize interval's left pointer and right pointer
_a , _a : List[Any] =... | 276 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A__: Dict = logging.get_logger(__name__)
A__: Tuple = {
'''weiweishi/roc-bert-base-zh''': '''https://huggingface.co/weiweishi/roc-bert-base-zh/reso... | 276 | 1 |
'''simple docstring'''
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_... | 356 |
'''simple docstring'''
def __lowerCamelCase ( lowerCAmelCase_ ) -> list:
if any(not isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) or x < 0 for x in sequence ):
raise TypeError('Sequence must be list of non-negative integers' )
for _ in range(len(lowerCAmelCase_ ) ):
... | 107 | 0 |
import json
import os
from typing import Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_lowerCamelCase : Tuple = logging.get_logger(__name__)
_lowerCamelCase : Optional[Any] = {
"""vocab_file""": """voc... | 14 | '''simple docstring'''
import gc
import inspect
import unittest
import torch
from parameterized import parameterized
from diffusers import PriorTransformer
from diffusers.utils import floats_tensor, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_m... | 239 | 0 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
i... | 31 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowercase: str = logging.get_logger(__name__)
__lowercase: Optional[Any] = {
"abeja/gpt-neox-japanese-2.7b": "https://huggingface.co/abeja/gpt-neox-japanese-2.7b/r... | 31 | 1 |
lowercase_ = "Alexander Joslin"
import operator as op
from .stack import Stack
def _snake_case( SCREAMING_SNAKE_CASE__ : str ) -> int:
'''simple docstring'''
A__ = {'*': op.mul, '/': op.truediv, '+': op.add, '-': op.sub}
A_... | 7 |
"""simple docstring"""
def _SCREAMING_SNAKE_CASE ( __snake_case : int = 1_00 ):
'''simple docstring'''
lowercase = 0
lowercase = 0
for i in range(1 , n + 1 ):
sum_of_squares += i**2
sum_of_ints += i
return sum_of_ints**2 - sum_... | 220 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_tf_available,
is_torch_available,
)
__A : List[str] = {
'configuration_speech_to_text': ['SPEECH_TO_TEXT_PRETRAINED_CONFIG_A... | 371 |
"""simple docstring"""
import json
import os
import shutil
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoConfig, BertConfig, GPTaConfig
from... | 57 | 0 |
import inspect
import unittest
import warnings
from math import ceil, floor
from transformers import LevitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vis... | 210 | import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a : str = logging.get_logger(__name__)
__a : int = {
"""microsoft/wavlm-base""": """https://huggingface.co/microsoft/wavlm-base/resolve/main/config.json""",
# See all W... | 210 | 1 |
"""simple docstring"""
import warnings
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import comp... | 353 |
"""simple docstring"""
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelerate... | 23 | 0 |
'''simple docstring'''
from math import factorial
lowerCAmelCase_ : Optional[int] = {str(d): factorial(d) for d in range(10)}
def _lowerCamelCase ( lowercase : int ) -> int:
return sum(DIGIT_FACTORIAL[d] for d in str(lowercase ) )
def _lowerCame... | 63 |
import argparse
import csv
import logging
import os
import random
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange
from transformers import (
CONFIG_NAME,
WEIGHTS_NAME,
AdamW,
... | 274 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections import Counter
from random import random
class A_ :
'''simple docstring'''
def __init__( self : List[Any] ) -> Union[str, Any]:
UpperCAmelCase : List[str] = ... | 280 |
'''simple docstring'''
import argparse
import json
from tqdm import tqdm
def UpperCamelCase( ):
UpperCAmelCase : List[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--src_path' , type=UpperCAmelCase_ , default='biencoder-nq-dev.json' ... | 280 | 1 |
import math
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ = {
'''facebook/data2vec-base-960h''': '''https://huggingface.co/facebook/data2vec-audio-base-960h/resolve/main/config.json''',
# S... | 244 |
from collections.abc import Generator
def __magic_name__ ( ):
'''simple docstring'''
UpperCamelCase__ , UpperCamelCase__ = 0, 1
while True:
UpperCamelCase__ , UpperCamelCase__ = b, a + b
yield b
def __magic_name__ ( __a ... | 244 | 1 |
'''simple docstring'''
from __future__ import annotations
def _SCREAMING_SNAKE_CASE ( UpperCamelCase ):
"""simple docstring"""
if not nums:
raise ValueError("""List is empty""" )
return sum(UpperCamelCase ) / len(UpperCamelCase )
if __name__ == "__main_... | 184 |
'''simple docstring'''
import unittest
import torch
from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMixin, UNetTesterMixin
enable_full_determinism()
cl... | 184 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
a__ : List[Any] =logging.get_logger(__name__)
a__ : Tuple ={
'''microsoft/focal... | 53 |
'''simple docstring'''
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version('''>=''', '''4.25.0''')):
raise Option... | 53 | 1 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale... | 355 |
"""simple docstring"""
import argparse
import torch
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt
if __name__ == "__main__":
lowercase__ = argparse.ArgumentParser()
parser.add_argument(
"... | 12 | 0 |
import numpy as np
def A_ ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = 1e-12 , _lowerCAmelCase = 100 , ) -> tuple[float, np.ndarray]:
assert np.shape(_lowerCAmelCase )[0] == np.shape(_lowerCAmelCase )[1]
# Ensure proper dimensionality.
assert ... | 52 |
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pytest
from datasets.arrow_dataset import Dataset
from datasets.search import ElasticSearchIndex, FaissIndex, MissingIndex
from .utils import require_elasticsearch, re... | 52 | 1 |
from math import factorial
def a ( SCREAMING_SNAKE_CASE_ : int = 1_0_0 ):
"""simple docstring"""
return sum(int(lowerCAmelCase__ ) for x in str(factorial(lowerCAmelCase__ ) ) )
if __name__ == "__main__":
print(solution(int(input(... | 366 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__UpperCAmelCase : Tuple = logging.get_logger(__name__)
__UpperCAmelCase : Union[str, Any] = {
"kssteven/i... | 315 | 0 |
"""simple docstring"""
import argparse
import os
import re
lowercase_ = "src/transformers/models/auto"
# re pattern that matches mapping introductions:
# SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
lowercase_ = re.compile(r"[A-Z_]+_MAPPING... | 45 |
import os
def a ( ):
'''simple docstring'''
lowercase_ = os.path.join(os.path.dirname(snake_case__ ) , '''num.txt''' )
with open(snake_case__ ) as file_hand:
return str(sum(int(snake_case__ ) for line in file_hand ) )[:10]
... | 30 | 0 |
import os
_SCREAMING_SNAKE_CASE = {"""I""": 1, """V""": 5, """X""": 10, """L""": 50, """C""": 1_00, """D""": 5_00, """M""": 10_00}
def SCREAMING_SNAKE_CASE__ ( __a ):
snake_case_ : Union[str, Any] = 0
snake_case_ : Union[str, Any] = 0
while ... | 88 |
from __future__ import annotations
import pandas as pd
def SCREAMING_SNAKE_CASE__ ( __a , __a , __a ):
snake_case_ : Optional[Any] = [0] * no_of_processes
snake_case_ : Tuple = [0] * no_of_processes
# Copy the burst time into remaining_time[]
... | 88 | 1 |
'''simple docstring'''
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,
)
lowerCAmelCase__ = {
'''configur... | 104 |
def a_ ( lowerCAmelCase_ : int ):
if p < 2:
raise ValueError('p should not be less than 2!' )
elif p == 2:
return True
__lowerCAmelCase = 4
__lowerCAmelCase = (1 << p) - 1
for _ in range(p - 2 ):
__lowerCAmelCase = ((s * s) - 2) % m
return s =... | 284 | 0 |
import os
import pytest
from attr import dataclass
__lowerCamelCase = """us-east-1""" # defaults region
@dataclass
class UpperCAmelCase :
A__ : Union[str, Any] = 42
A__ : Optional[Any] = "arn:aws:iam::558105141721:role/sagemaker_execution_role"
... | 368 |
import os
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_doctest_list.py
__lowerCamelCase = """."""
if __name__ == "__main__":
__lowerCamelCase = os.path.join(REPO_PATH, """utils/documentation_tests.txt""")
... | 10 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase : List[Any] = {
'configuration_pegasus_x': ['PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PegasusXConfig'],
}
try:
if not is_torch_availab... | 2 |
'''simple docstring'''
from math import isclose, sqrt
def snake_case_ ( _lowerCAmelCase : float , _lowerCAmelCase : float , _lowerCAmelCase : float ) -> tuple[float, float, float]:
UpperCAmelCase : Optional[int] = point_y /... | 23 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ : str = logging.get_logger(__name__)
lowercase__ : List[str] = {}
class a__ ( UpperCamelCase__ ):
a : Union[str, Any] = "llama"
a : Union[str, Any] = ... | 353 |
def SCREAMING_SNAKE_CASE ( __UpperCamelCase) -> int:
return 1 if digit in (0, 1) else (digit * factorial(digit - 1))
def SCREAMING_SNAKE_CASE ( __UpperCamelCase) -> bool:
a = 0
a = number
while duplicate > 0:
a , a = ... | 180 | 0 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transformers.utils im... | 43 | """simple docstring"""
import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional
import pyarrow as pa
import pyarrow.json as paj
import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline
_a : i... | 172 | 0 |
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from transformers import (
VisualBertConfig,
VisualBertForMultipleChoice,
VisualBertForPreTraining,
VisualBertForQuestionAnswering,
VisualBertForVisualReasoning,
)
from transformers.utils import logging
l... | 278 |
import warnings
from ...utils import logging
from .image_processing_deformable_detr import DeformableDetrImageProcessor
__A = logging.get_logger(__name__)
class lowercase_ ( __lowercase ):
def __init__( self : Optional[Any] , *A__ : List[Any] , **A__ : ... | 278 | 1 |
import argparse
import torch
# Step 1. clone https://github.com/microsoft/unilm
# Step 2. git checkout to https://github.com/microsoft/unilm/commit/b94ec76c36f02fb2b0bf0dcb0b8554a2185173cd
# Step 3. cd unilm
# Step 4. ln -s $(realpath wavlm/modules.py) ./ # create simlink
# import classes
from unilm.wavlm.WavL... | 209 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ = {
'''EleutherAI/gpt-neox-20b''': '''https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.... | 79 | 0 |
import sys
from pathlib import Path
SCREAMING_SNAKE_CASE : Tuple = Path(__file__).resolve().parents[3] / "src"
sys.path.insert(1, str(git_repo_path))
import dataclasses # noqa
import io # noqa
import itertools # noqa
import json # noqa
import os # noqa
import unittest # noqa
from copy impo... | 362 |
def __magic_name__ ( __lowerCAmelCase : int , __lowerCAmelCase : int ) -> str:
if a < 0 or b < 0:
raise ValueError('''the value of both inputs must be positive''' )
__lowerCamelCase = str(bin(__lowerCAmelCase ) )[2:] # remove th... | 339 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
_lowerCAmelCase = {
'''configuration_data2vec_audio''': ['''DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Data... | 298 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils impo... | 12 | 0 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_deit import DeiTImageProcessor
lowercase_ = logging.get_logger(__name__)
class __lowerCAmelCase ( snake_case_ ):
'''simple docstring'''
def __init__( self , *_a ... | 355 |
"""simple docstring"""
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, PyTorch... | 11 | 0 |
"""simple docstring"""
import requests
from bsa import BeautifulSoup
def __magic_name__ ( lowercase = "https://www.worldometers.info/coronavirus" ):
SCREAMING_SNAKE_CASE_: List[str] =BeautifulSoup(requests.get(lowercase ).text , """html.parser""" )
... | 173 |
"""simple docstring"""
import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image
from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import ... | 173 | 1 |
import unittest
from transformers import MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, AutoTokenizer, is_vision_available
from transformers.pipelines import pipeline
from transformers.pipelines.document_question_answering import apply_tesseract
from transformers.testing_utils import (
is_pipeline_test,
... | 70 |
# flake8: noqa
# Lint as: python3
from typing import Dict, List, Optional, Type
from .. import config
from ..utils import logging
from .formatting import (
ArrowFormatter,
CustomFormatter,
Formatter,
PandasFormatter,
PythonFormatter,
TensorFormatter,
format_table,
query_table,
)
... | 70 | 1 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Tuple
import numpy as np
import torch
@dataclass
class UpperCAmelCase :
_lowercase: int = 42 # [batch_size x 3]
_lowercase: Tuple = 42 # [batch_size x 3]
... | 70 |
'''simple docstring'''
from typing import List
from .keymap import KEYMAP, get_character
def lowerCamelCase ( __lowerCamelCase : str ) ->Optional[int]:
def decorator(__lowerCamelCase : int ):
_SCREAMING_SNAKE_CASE = getattr(__lowerCamelCase , """ha... | 58 | 0 |
"""simple docstring"""
from __future__ import annotations
from sys import maxsize
from typing import Generic, TypeVar
lowerCAmelCase__ = TypeVar('''T''')
def a__ ( SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
return (position - 1) // 2
def a__ ( SCR... | 359 |
"""simple docstring"""
import unittest
from transformers import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, is_vision_available, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipeli... | 133 | 0 |
'''simple docstring'''
import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch
from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search
from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
from u... | 85 |
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 ..utils import assert_arrow_... | 10 | 0 |
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def SCREAMING_SNAKE_CASE__ ( ) -> str:
snake_case_ : Tuple = ArgumentParser(
description=(
'P... | 370 |
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class SCREAMING_SNAKE_CASE_ ( snake_case_ , snake_case_ ):
@register_to_config
def __init__( self : Optional[Any] , *,
... | 88 | 0 |
from collections import OrderedDict
from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES
UpperCamelCase__ = logging.get_logger(__name__)
UpperCamelCase__ = OrderedDict(
[
... | 92 |
'''simple docstring'''
import itertools
import random
import unittest
import numpy as np
from transformers import WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST, WavaVecaConfig, WavaVecaFeatureExtractor
from transformers.testing_utils import require_torch, slow
from ...test_sequence_feature_extraction_common import S... | 83 | 0 |
'''simple docstring'''
import dataclasses
import re
import string
from typing import Any, Dict, Iterator, List, Mapping, Optional, Sequence, Tuple
import numpy as np
from . import residue_constants
_SCREAMING_SNAKE_CASE = Mapping[str, np.ndarray]
_SCREAMING_SNAKE_CASE = M... | 217 |
'''simple docstring'''
_SCREAMING_SNAKE_CASE = {
'''A''': ['''B''', '''C''', '''E'''],
'''B''': ['''A''', '''D''', '''E'''],
'''C''': ['''A''', '''F''', '''G'''],
'''D''': ['''B'''],
'''E''': ['''A''', '''B''', '''D'''],
'''F''': ['''C'''],
'''G''': ['''C''']... | 217 | 1 |
from math import sqrt
def _snake_case( SCREAMING_SNAKE_CASE__ ) -> bool:
assert isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) and (
number >= 0
), "'number' must been an int and positive"
lowercase : Union[str, Any] = True... | 20 |
import argparse
import hashlib # hashlib is only used inside the Test class
import struct
class A__ :
def __init__( self : Dict , a : str ):
'''simple docstring'''
lowerCAmelCase__ : Optio... | 212 | 0 |
"""simple docstring"""
from __future__ import annotations
import random
import unittest
from transformers import TransfoXLConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFMo... | 369 | """simple docstring"""
import copy
import unittest
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
... | 126 | 0 |
from typing import Optional
import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
... | 149 |
import requests
from bsa import BeautifulSoup
def lowerCAmelCase_ ( A_ = "https://www.worldometers.info/coronavirus"):
UpperCamelCase__: Union[str, Any] = BeautifulSoup(requests.get(A_).text ,"html.parser")
UpperCamelCase__: Tuple = soup.fi... | 149 | 1 |
from functools import reduce
lowercase_ = (
"""73167176531330624919225119674426574742355349194934"""
"""96983520312774506326239578318016984801869478851843"""
"""85861560789112949495459501737958331952853208805511"""
"""12540698747158523863050715693290963295227443043557"""
... | 269 |
class SCREAMING_SNAKE_CASE (UpperCAmelCase ):
pass
class SCREAMING_SNAKE_CASE (UpperCAmelCase ):
pass
class SCREAMING_SNAKE_CASE :
def __init__( self : Dict )-> Optional[int]:
"""simple docstring"""... | 269 | 1 |
import numpy
# List of input, output pairs
_UpperCAmelCase : Union[str, Any] = (
((5, 2, 3), 15),
((6, 5, 9), 25),
((11, 12, 13), 41),
((1, 1, 1), 8),
((11, 12, 13), 41),
)
_UpperCAmelCase : Dict = (((5_15, 22, 13), 5_55), ((61, 35, 49), 1_50))
_UpperCAmelCase ... | 50 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_... | 11 | 0 |
'''simple docstring'''
def a_ ( __snake_case : list[int] ) -> int:
"""simple docstring"""
if not numbers:
return 0
if not isinstance(UpperCAmelCase__ , (list, tuple) ) or not all(
isinstance(UpperCAmelCase__... | 370 |
'''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 __UpperCamelCase ( ... | 6 | 0 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_electra import ElectraTokenizer
SCREAMING_SNAKE_CASE : str = {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.jso... | 21 |
def _SCREAMING_SNAKE_CASE ( lowercase : int , lowercase : int ):
'''simple docstring'''
return int((input_a, input_a).count(1 ) != 0 )
def _SCREAMING_SNAKE_CASE ( ):
'''simple docstring'''
assert or_gate(0 ,... | 204 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase_ = {
'configuration_xlm_roberta_xl': [
'XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP',
'XLMRobertaXLConfig',
'XLMRobertaXL... | 247 |
import qiskit
def lowerCAmelCase_ ( __UpperCAmelCase: int , __UpperCAmelCase: int ) -> qiskit.result.counts.Counts:
UpperCamelCase__ : Optional[Any] = qiskit.Aer.get_backend('''aer_simulator''' )
# Create a Quantum Circuit acting on ... | 247 | 1 |
"""simple docstring"""
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_s... | 113 |
'''simple docstring'''
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List
import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor
from transformers import... | 58 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__snake_case : List[str] ={
'configuration_x_clip': [
'XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
'XCLIPConfig',
'XCLIPTextConfig',
'XCLIPVisionConfig',
],... | 94 |
__snake_case : Any ='\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'
__snake_case : Tuple =[{'type':... | 94 | 1 |
import copy
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
a =logging.get_logger(__name__)
a ={
""... | 73 |
'''simple docstring'''
import pytest
from datasets import inspect_metric, list_metrics, load_metric
@pytest.fixture
def UpperCAmelCase_ ( __lowerCamelCase : List[str] ):
monkeypatch.setattr("datasets.utils.deprecation_utils._emitted_deprecation_warnings" ,set() )
@pytest.... | 223 | 0 |
import math
def A ( lowercase = 100 ) -> int:
'''simple docstring'''
UpperCamelCase = sum(i * i for i in range(1 , n + 1 ) )
UpperCamelCase = int(math.pow(sum(range(1 , n + 1 ) ) , 2 ) )
return square_of_sum - sum_of_squares
if __name__ == "__main__... | 110 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version(">=", "4.25.0")):
raise OptionalDependencyNotAvailable()
except... | 110 | 1 |
"""simple docstring"""
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class __Uppe... | 105 |
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 datasets import load_dataset, l... | 146 | 0 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class lowerCamelCase ( metaclass=_lowerCAmelCase ):
lowercase : str = ["keras_nlp"]
def __init__( self , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ):
requires_backe... | 369 |
"""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_util... | 27 | 0 |
'''simple docstring'''
import argparse
import re
import requests
import torch
# git clone https://github.com/salesforce/BLIP.git
from models.blip import blip_decoder
from models.blip_itm import blip_itm
from models.blip_vqa import blip_vqa
from PIL import Image
from torchvision import transforms
from t... | 229 |
def __UpperCamelCase ( lowerCAmelCase__ : int = 5_0_0_0_0_0_0_0 ):
__a : int = set()
__a : str = int((limit - 2_4) ** (1 / 2) )
__a : int = set(range(3 , prime_square_limit + 1 , 2 ) )
primes.add(2 )
for p in range(3 , prime_square_limit + 1 , 2 ):
if p... | 216 | 0 |
"""simple docstring"""
import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional
import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto import tqdm
import transformers
from tr... | 56 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
a :Dict = {
"configuration_rag": ["RagConfig"],
"retrieval_rag": ["RagRetriever"],
"tokenization_rag": ["RagTokenizer"],
}
try:
if no... | 56 | 1 |
"""simple docstring"""
def _lowercase ( __snake_case ) -> bool:
__lowerCAmelCase : set[int] = set()
# To detect a back edge, keep track of vertices currently in the recursion stack
__lowerCAmelCase : set[int] = set()
return... | 269 |
"""simple docstring"""
import gc
import threading
import time
import psutil
import torch
class A__ :
'''simple docstring'''
def __init__( self: str) -> Optional[Any]:
"""simple docstring"""
__lowerCAmelCase : Optional[Any] ... | 269 | 1 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
import diffusers
from diffusers import (
AutoencoderKL,
EulerDiscreteScheduler,
StableDiffusionLatentUpscalePipeline,
StableDiffusion... | 365 | '''simple docstring'''
import os
import sys
from contextlib import contextmanager
# Windows only
if os.name == "nt":
import ctypes
import msvcrt # noqa
class __magic_name__ ( ctypes.Structure):
# _fields is a specific attr expected by ctypes
UpperCamelCase__ ... | 21 | 0 |
from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors
def UpperCamelCase_( lowerCamelCase_ ) -> int:
_lowercase : Optional[Any] = prime_factors(lowerCamelCase_ )
if is_square_free(lowerCamelCase_ ):
return -1 if len(lower... | 21 |
import html
from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from ...utils import is_bsa_available, logging, requires_backends
if is_bsa_available():
import bsa
from bsa import BeautifulSoup
A : str = logging.get_logger(__name__)
class __A( a ):
def... | 6 | 0 |
"""simple docstring"""
from collections import OrderedDict
from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES
__snake_case : int = logging.get_logger(__name__... | 58 |
"""simple docstring"""
from __future__ import annotations
from bisect import bisect_left
from functools import total_ordering
from heapq import merge
@total_ordering
class A__ ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __lt__( se... | 58 | 1 |
"""simple docstring"""
from datasets.utils.patching import _PatchedModuleObj, patch_submodule
from . import _test_patching
def _SCREAMING_SNAKE_CASE ( ) -> Optional[int]:
import os as original_os
from os import path as original_path
from os import rename as original_rename
from os.pa... | 247 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE = {
# See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}
class UpperCAmelCase_ ... | 247 | 1 |
"""simple docstring"""
def a__ ( __lowercase ) -> int:
if not grid or not grid[0]:
raise TypeError("The grid does not contain the appropriate information" )
for cell_n in range(1 , len(grid[0] ) ):
grid[0][cell_n] += grid[0][cell_n - 1]
_A = ... | 358 |
"""simple docstring"""
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
"t5-small": "https://huggingface.co/t5-smal... | 163 | 0 |
"""simple docstring"""
from __future__ import annotations
def UpperCAmelCase__ (lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ):
'''simple docstring'''
if len(lowerCAmelCase_ ) == 0:
raise ValueError("find_max() arg is an empty sequence" )
if... | 54 |
"""simple docstring"""
def UpperCAmelCase__ (lowerCAmelCase_ = 100_0000 ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = set(range(3 , lowerCAmelCase_ , 2 ) )
primes.add(2 )
for p in range(3 , lowerCAmelCase_ , 2 ):
if... | 54 | 1 |
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
... | 292 |
# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can a... | 292 | 1 |
"""simple docstring"""
from math import pow, sqrt
def _snake_case ( *snake_case__ : float ):
A = len(snake_case__ ) > 0 and all(value > 0.0 for value in values )
return result
def _snake_case ( snake_case__ : float , snake_case__ : float ... | 74 |
lowerCamelCase : Optional[Any] = '''ABCDEFGHIJKLMNOPQRSTUVWXYZ'''
def snake_case_ ( ):
__lowercase : List[str] = input("""Enter message: """ )
__lowercase : int = input("""Enter key [alphanumeric]: """ )
__lowercase : ... | 233 | 0 |
def _a ( SCREAMING_SNAKE_CASE__ : Optional[int] = 2_00_00_00 ) -> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Any = [0 for i in range(n + 1 )]
SCREAMING_SNAKE_CASE__ : Union[str, Any] = 1
SCR... | 363 |
_lowerCamelCase : dict[tuple[int, int, int], int] = {}
def _a ( SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int ) -> int:
'''simple docstring'''
if late == 3 or absent == 2:
... | 191 | 0 |
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