code stringlengths 82 54.1k | code_codestyle int64 0 699 | style_context stringlengths 111 35.6k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
from __future__ import annotations
import math
def SCREAMING_SNAKE_CASE_ ( _snake_case :int , _snake_case :int , _snake_case :bool , _snake_case :list[int] , _snake_case :float ) -> int:
if depth < 0:
raise ValueError('''Depth cannot be less than 0''' )
if len(... | 2 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = """▁"... | 2 | 1 |
import json
import os
import unittest
from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
VOCAB_FILES_NAMES,
BlenderbotSmallTokenizer,
)
from ...test_tokenization_common import TokenizerTesterMixin
class lowerCamelCase__ ( _A , unittest.TestCase... | 2 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE_ ( _snake_case :dict , _snake_case :str ) -> set[str]:
_A , _A = set(_snake_case ), [start]
while stack:
_A = stack.pop()
explored.add(_snake_case )
# Differences from B... | 2 | 1 |
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from PIL import Image
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler... | 2 |
from .configuration_bert_masked import MaskedBertConfig
from .modeling_bert_masked import (
MaskedBertForMultipleChoice,
MaskedBertForQuestionAnswering,
MaskedBertForSequenceClassification,
MaskedBertForTokenClassification,
MaskedBertModel,
)
from .modules import *
| 2 | 1 |
import json
import os
import re
import unicodedata
from json.encoder import INFINITY
from typing import Any, Dict, List, Optional, Tuple, Union
import numpy as np
import regex
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding
fro... | 2 |
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = {
"""xlnet-base-cased""": """https://huggingface.co/xlnet-base-cased/resolve/main/config.json""",
"""xlnet-large-cas... | 2 | 1 |
from typing import Optional
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import (
BackboneOutput,
BaseModelOutputWithNoAttention,
BaseM... | 2 |
def SCREAMING_SNAKE_CASE_ ( _snake_case :bytes ) -> str:
return "".join([hex(_snake_case )[2:].zfill(2 ).upper() for byte in list(_snake_case )] )
def SCREAMING_SNAKE_CASE_ ( _snake_case :str ) -> bytes:
# Check data validity, following RFC3548
# https://... | 2 | 1 |
import argparse
import glob
import importlib.util
import os
import re
import black
from doc_builder.style_doc import style_docstrings_in_code
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
UpperCAmelCase_ ... | 2 |
def SCREAMING_SNAKE_CASE_ ( _snake_case :list ) -> bool:
if not isinstance(_snake_case , _snake_case ):
raise ValueError('''Input series is not valid, valid series - [2, 4, 6]''' )
if len(_snake_case ) == 0:
raise ValueError('''Input list must be a non empty list'... | 2 | 1 |
import argparse
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration
UpperCAmelCase_ = [
# tf -> hf
("""/""", """."""),
("""layer_""", """layers."""),
("""k... | 2 |
import math
import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def SCREAMING_SNAKE_CASE_ ( _snake_case :int = 3 ) -> qiskit.result.counts.Counts:
if isinstance(_snake_case , _snake_case ):
raise TypeError(''... | 2 | 1 |
UpperCAmelCase_ = [
(1_0_0_0, """M"""),
(9_0_0, """CM"""),
(5_0_0, """D"""),
(4_0_0, """CD"""),
(1_0_0, """C"""),
(9_0, """XC"""),
(5_0, """L"""),
(4_0, """XL"""),
(1_0, """X"""),
(9, """IX"""),
(5, """V"""),
(4, """IV"""),
(1, """I"""),
]
... | 2 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
# Register SEW's fairseq modules
from sew_asapp import tasks # noqa: F401
from transformers import (
SEWConfig,
SEWForCTC,
SEWModel,
WavaVecaCTCTokenizer,
WavaVecaFeature... | 2 | 1 |
import collections
import os
import re
from pathlib import Path
UpperCAmelCase_ = """src/transformers"""
# Matches is_xxx_available()
UpperCAmelCase_ = re.compile(r"""is\_([a-z_]*)_available()""")
# Catches a one-line _import_struct = {xxx}
UpperCAmelCase_ = re.compile(r"""^_im... | 2 |
import unittest
from transformers import is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
... | 2 | 1 |
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import ... | 2 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transfo... | 2 | 1 |
def SCREAMING_SNAKE_CASE_ ( _snake_case :str ) -> bool:
_A = [int(_snake_case ) for i in ip_va_address.split('''.''' ) if i.isdigit()]
return len(_snake_case ) == 4 and all(0 <= int(_snake_case ) <= 254 for octet in octets )
if __name__ == "__main__":
Up... | 2 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = {"""openai-gpt""": """https://huggingface.co/openai-gpt/resolve/main/config.json"""}
class lowerCamelCase__ ( _A):
"""simple ... | 2 | 1 |
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelC... | 2 |
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_in... | 2 | 1 |
import numpy as np
import torch
import tqdm
from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler
class lowerCamelCase__ ( _A):
"""simple docstring"""
def __init__... | 2 |
UpperCAmelCase_ = 0 # The first color of the flag.
UpperCAmelCase_ = 1 # The second color of the flag.
UpperCAmelCase_ = 2 # The third color of the flag.
UpperCAmelCase_ = (red, white, blue)
def SCREAMING_SNAKE_CASE_ ( _snake_case :list ) -> list:
if not seque... | 2 | 1 |
from __future__ import annotations
import math
def SCREAMING_SNAKE_CASE_ ( _snake_case :int ) -> bool:
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples... | 2 |
import itertools
import math
def SCREAMING_SNAKE_CASE_ ( _snake_case :int ) -> bool:
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes... | 2 | 1 |
import json
import os
import re
import unittest
from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import Tokeni... | 2 |
import collections
import os
import re
from pathlib import Path
UpperCAmelCase_ = """src/transformers"""
# Matches is_xxx_available()
UpperCAmelCase_ = re.compile(r"""is\_([a-z_]*)_available()""")
# Catches a one-line _import_struct = {xxx}
UpperCAmelCase_ = re.compile(r"""^_im... | 2 | 1 |
import collections
import inspect
import unittest
from transformers import SwinvaConfig
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 imp... | 2 |
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from .... | 2 | 1 |
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import datasets
import datasets.config
from .utils import require_beam
class lowerCamelCase__ ( datasets.BeamBasedBuilder):
"""simple docstring"""
def snak... | 2 |
import requests
from bsa import BeautifulSoup
def SCREAMING_SNAKE_CASE_ ( _snake_case :str = "AAPL" ) -> str:
_A = F'''https://in.finance.yahoo.com/quote/{symbol}?s={symbol}'''
_A = BeautifulSoup(requests.get(_snake_case ).text , '''html.parser''' )
_... | 2 | 1 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, Features, Value
from .base import TaskTemplate
@dataclass(frozen=_A)
class lowerCamelCase__ ( _A):
"""simple docstring"""
a__ : str = field(default="automatic-... | 2 |
from graphs.minimum_spanning_tree_kruskal import kruskal
def SCREAMING_SNAKE_CASE_ ( ) -> Tuple:
_A = 9
_A = [
[0, 1, 4],
[0, 7, 8],
[1, 2, 8],
[7, 8, 7],
[7, 6, 1],
[2, 8, 2],
[8, 6, 6],
[2, 3, 7],
... | 2 | 1 |
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class lowerCamelCase__ ( _A):
... | 2 |
def SCREAMING_SNAKE_CASE_ ( _snake_case :int ) -> int:
if not isinstance(_snake_case , _snake_case ):
raise ValueError('''Input must be an integer''' )
if input_num <= 0:
raise ValueError('''Input must be positive''' )
return sum(
divisor for divisor i... | 2 | 1 |
import multiprocessing
from typing import TYPE_CHECKING, Optional, Union
from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream
if TYPE_CHECKING:
import sqli... | 2 |
UpperCAmelCase_ = 2_5_6
# Modulus to hash a string
UpperCAmelCase_ = 1_0_0_0_0_0_3
def SCREAMING_SNAKE_CASE_ ( _snake_case :str , _snake_case :str ) -> bool:
_A = len(_snake_case )
_A = len(_snake_case )
if p_len > t_len:
return Fa... | 2 | 1 |
import math
from typing import Optional
import numpy as np
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = {
"""facebook/encodec_24khz""": """https://huggingface.co/facebook/encodec... | 2 |
import json
import os
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = {
"""vocab_file""": """vocab.json""",
"""tokenizer_config... | 2 | 1 |
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,
O... | 2 |
from __future__ import annotations
from sys import maxsize
from typing import Generic, TypeVar
UpperCAmelCase_ = TypeVar("""T""")
def SCREAMING_SNAKE_CASE_ ( _snake_case :int ) -> int:
return (position - 1) // 2
def SCREAMING_SNAKE_CASE_ ( _snake_case :int ) -> ... | 2 | 1 |
import io
import math
from typing import Dict, Optional, Union
import numpy as np
from huggingface_hub import hf_hub_download
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import convert_to_rgb, normalize, to_channel_dimension_format, to_pil_image
fro... | 2 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = """▁"... | 2 | 1 |
from ....configuration_utils import PretrainedConfig
from ....utils import logging
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = {
"""speechbrain/m-ctc-t-large""": """https://huggingface.co/speechbrain/m-ctc-t-large/resolve/main/config.json""",
# See all M-CTC-T... | 2 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE_ ( _snake_case :dict , _snake_case :str ) -> set[str]:
_A , _A = set(_snake_case ), [start]
while stack:
_A = stack.pop()
explored.add(_snake_case )
# Differences from B... | 2 | 1 |
from __future__ import annotations
from collections import deque
class lowerCamelCase__ :
"""simple docstring"""
def __init__( self : int , __lowerCAmelCase : list[str] ) -> Union[str, Any]:
_A = []
self.adlist.append(
{'''value... | 2 |
from .configuration_bert_masked import MaskedBertConfig
from .modeling_bert_masked import (
MaskedBertForMultipleChoice,
MaskedBertForQuestionAnswering,
MaskedBertForSequenceClassification,
MaskedBertForTokenClassification,
MaskedBertModel,
)
from .modules import *
| 2 | 1 |
UpperCAmelCase_ = [sum(int(c, 1_0) ** 2 for c in i.__str__()) for i in range(1_0_0_0_0_0)]
def SCREAMING_SNAKE_CASE_ ( _snake_case :int ) -> int:
_A = 0
while number:
# Increased Speed Slightly by checking every 5 digits together.
sum_of_digits_squared +=... | 2 |
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = {
"""xlnet-base-cased""": """https://huggingface.co/xlnet-base-cased/resolve/main/config.json""",
"""xlnet-large-cas... | 2 | 1 |
import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def SCREAMING_SNAKE_CASE_ ( _sna... | 2 |
def SCREAMING_SNAKE_CASE_ ( _snake_case :bytes ) -> str:
return "".join([hex(_snake_case )[2:].zfill(2 ).upper() for byte in list(_snake_case )] )
def SCREAMING_SNAKE_CASE_ ( _snake_case :str ) -> bytes:
# Check data validity, following RFC3548
# https://... | 2 | 1 |
# 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 requi... | 2 |
def SCREAMING_SNAKE_CASE_ ( _snake_case :list ) -> bool:
if not isinstance(_snake_case , _snake_case ):
raise ValueError('''Input series is not valid, valid series - [2, 4, 6]''' )
if len(_snake_case ) == 0:
raise ValueError('''Input list must be a non empty list'... | 2 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
UpperCAmelCase_ = {
"""configuration_mobilevit""": ["""MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Mobi... | 2 |
import math
import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def SCREAMING_SNAKE_CASE_ ( _snake_case :int = 3 ) -> qiskit.result.counts.Counts:
if isinstance(_snake_case , _snake_case ):
raise TypeError(''... | 2 | 1 |
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
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = {
"""goo... | 2 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
# Register SEW's fairseq modules
from sew_asapp import tasks # noqa: F401
from transformers import (
SEWConfig,
SEWForCTC,
SEWModel,
WavaVecaCTCTokenizer,
WavaVecaFeature... | 2 | 1 |
import sys
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be check... | 2 |
import unittest
from transformers import is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
... | 2 | 1 |
import argparse
import os
import pickle
import sys
import torch
from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl im... | 2 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transfo... | 2 | 1 |
import os
import pytest
from attr import dataclass
UpperCAmelCase_ = """us-east-1""" # defaults region
@dataclass
class lowerCamelCase__ :
"""simple docstring"""
a__ : str
a__ : List[str] = "arn:aws:iam::558105141721:role/sagemaker_execution_role"
a__ : List[A... | 2 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = {"""openai-gpt""": """https://huggingface.co/openai-gpt/resolve/main/config.json"""}
class lowerCamelCase__ ( _A):
"""simple ... | 2 | 1 |
from collections import namedtuple
import requests
from lxml import html # type: ignore
UpperCAmelCase_ = namedtuple("""covid_data""", """cases deaths recovered""")
def SCREAMING_SNAKE_CASE_ ( _snake_case :str = "https://www.worldometers.info/coronavirus/" ) -> covid_data:
_A... | 2 |
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_in... | 2 | 1 |
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,
M... | 2 |
UpperCAmelCase_ = 0 # The first color of the flag.
UpperCAmelCase_ = 1 # The second color of the flag.
UpperCAmelCase_ = 2 # The third color of the flag.
UpperCAmelCase_ = (red, white, blue)
def SCREAMING_SNAKE_CASE_ ( _snake_case :list ) -> list:
if not seque... | 2 | 1 |
import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate... | 2 |
import itertools
import math
def SCREAMING_SNAKE_CASE_ ( _snake_case :int ) -> bool:
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes... | 2 | 1 |
from abc import ABC, abstractmethod
from typing import List, Optional
class lowerCamelCase__ ( _A):
"""simple docstring"""
def __init__( self : Dict ) -> Tuple:
# test for the above condition
self.test()
def snake_case_ ( self : List[s... | 2 |
import collections
import os
import re
from pathlib import Path
UpperCAmelCase_ = """src/transformers"""
# Matches is_xxx_available()
UpperCAmelCase_ = re.compile(r"""is\_([a-z_]*)_available()""")
# Catches a one-line _import_struct = {xxx}
UpperCAmelCase_ = re.compile(r"""^_im... | 2 | 1 |
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():
... | 2 |
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from .... | 2 | 1 |
import darl # noqa
import gym
import tqdm
from diffusers.experimental import ValueGuidedRLPipeline
UpperCAmelCase_ = {
"""n_samples""": 6_4,
"""horizon""": 3_2,
"""num_inference_steps""": 2_0,
"""n_guide_steps""": 2, # can set to 0 for faster sampling, does not use value netw... | 2 |
import requests
from bsa import BeautifulSoup
def SCREAMING_SNAKE_CASE_ ( _snake_case :str = "AAPL" ) -> str:
_A = F'''https://in.finance.yahoo.com/quote/{symbol}?s={symbol}'''
_A = BeautifulSoup(requests.get(_snake_case ).text , '''html.parser''' )
_... | 2 | 1 |
import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation
def SCREAMING_SNAKE_CASE_ ( _snake_case :int = 8 ) -> str:
_A = ascii_letters + digits + punctuation
return "".join(secrets.choice(_snake_case ... | 2 |
from graphs.minimum_spanning_tree_kruskal import kruskal
def SCREAMING_SNAKE_CASE_ ( ) -> Tuple:
_A = 9
_A = [
[0, 1, 4],
[0, 7, 8],
[1, 2, 8],
[7, 8, 7],
[7, 6, 1],
[2, 8, 2],
[8, 6, 6],
[2, 3, 7],
... | 2 | 1 |
from __future__ import annotations
UpperCAmelCase_ = [
[-1, 0], # left
[0, -1], # down
[1, 0], # right
[0, 1], # up
]
def SCREAMING_SNAKE_CASE_ ( _snake_case :list[list[int]] , _snake_case :list[int] , _snake_case :list[int] , _snake_case :int , _snake_case :l... | 2 |
def SCREAMING_SNAKE_CASE_ ( _snake_case :int ) -> int:
if not isinstance(_snake_case , _snake_case ):
raise ValueError('''Input must be an integer''' )
if input_num <= 0:
raise ValueError('''Input must be positive''' )
return sum(
divisor for divisor i... | 2 | 1 |
def SCREAMING_SNAKE_CASE_ ( _snake_case :int ) -> list:
_A = int(_snake_case )
if n_element < 1:
_A = ValueError('''a should be a positive number''' )
raise my_error
_A = [1]
_A , _A , _A = (0, 0, 0)
_A ... | 2 |
UpperCAmelCase_ = 2_5_6
# Modulus to hash a string
UpperCAmelCase_ = 1_0_0_0_0_0_3
def SCREAMING_SNAKE_CASE_ ( _snake_case :str , _snake_case :str ) -> bool:
_A = len(_snake_case )
_A = len(_snake_case )
if p_len > t_len:
return Fa... | 2 | 1 |
import copy
from typing import Dict, List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
UpperCAmelCase_ = {
"""facebook/mask2former-swin-small-coco-instance""": (
"""https://huggingface.co/facebook/mask... | 2 |
import json
import os
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = {
"""vocab_file""": """vocab.json""",
"""tokenizer_config... | 2 | 1 |
import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression
# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
# Fitting Polynomial Regression to the dataset
from sklearn.preprocessing import Po... | 2 |
from __future__ import annotations
from sys import maxsize
from typing import Generic, TypeVar
UpperCAmelCase_ = TypeVar("""T""")
def SCREAMING_SNAKE_CASE_ ( _snake_case :int ) -> int:
return (position - 1) // 2
def SCREAMING_SNAKE_CASE_ ( _snake_case :int ) -> ... | 2 | 1 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transfo... | 2 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = """▁"... | 2 | 1 |
from math import factorial
def SCREAMING_SNAKE_CASE_ ( _snake_case :int = 100 ) -> int:
return sum(int(_snake_case ) for x in str(factorial(_snake_case ) ) )
if __name__ == "__main__":
print(solution(int(input("""Enter the Number: """).strip())))
| 2 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE_ ( _snake_case :dict , _snake_case :str ) -> set[str]:
_A , _A = set(_snake_case ), [start]
while stack:
_A = stack.pop()
explored.add(_snake_case )
# Differences from B... | 2 | 1 |
def SCREAMING_SNAKE_CASE_ ( _snake_case :int = 100 ) -> int:
_A = set()
_A = 0
_A = n + 1 # maximum limit
for a in range(2 , _snake_case ):
for b in range(2 , _snake_case ):
_A = a**b # calculates the c... | 2 |
from .configuration_bert_masked import MaskedBertConfig
from .modeling_bert_masked import (
MaskedBertForMultipleChoice,
MaskedBertForQuestionAnswering,
MaskedBertForSequenceClassification,
MaskedBertForTokenClassification,
MaskedBertModel,
)
from .modules import *
| 2 | 1 |
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
fro... | 2 |
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = {
"""xlnet-base-cased""": """https://huggingface.co/xlnet-base-cased/resolve/main/config.json""",
"""xlnet-large-cas... | 2 | 1 |
import argparse
import math
import os
import torch
from neural_compressor.utils.pytorch import load
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, StableDiffusionPipeline, UNetaDConditionModel
def SCREAMING_SNAKE_CASE_ ( ) -... | 2 |
def SCREAMING_SNAKE_CASE_ ( _snake_case :bytes ) -> str:
return "".join([hex(_snake_case )[2:].zfill(2 ).upper() for byte in list(_snake_case )] )
def SCREAMING_SNAKE_CASE_ ( _snake_case :str ) -> bytes:
# Check data validity, following RFC3548
# https://... | 2 | 1 |
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
from .record_evaluation import evaluate as evaluate_record
UpperCAmelCase_ = """\
@article{wang2019superglue,
title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},
author={Wang, Ale... | 2 |
def SCREAMING_SNAKE_CASE_ ( _snake_case :list ) -> bool:
if not isinstance(_snake_case , _snake_case ):
raise ValueError('''Input series is not valid, valid series - [2, 4, 6]''' )
if len(_snake_case ) == 0:
raise ValueError('''Input list must be a non empty list'... | 2 | 1 |
import json
import os
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = {
"""vocab_file""": """vocab.json""",
"""tokenizer_config... | 2 |
import math
import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def SCREAMING_SNAKE_CASE_ ( _snake_case :int = 3 ) -> qiskit.result.counts.Counts:
if isinstance(_snake_case , _snake_case ):
raise TypeError(''... | 2 | 1 |
def SCREAMING_SNAKE_CASE_ ( _snake_case :int = 50 ) -> int:
_A = [1] * (length + 1)
for row_length in range(3 , length + 1 ):
for block_length in range(3 , row_length + 1 ):
for block_start in range(row_length - block_length ):
ways_... | 2 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
# Register SEW's fairseq modules
from sew_asapp import tasks # noqa: F401
from transformers import (
SEWConfig,
SEWForCTC,
SEWModel,
WavaVecaCTCTokenizer,
WavaVecaFeature... | 2 | 1 |
from functools import lru_cache
def SCREAMING_SNAKE_CASE_ ( _snake_case :int ) -> set:
_A = 2
_A = set()
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.add(_snake_case )
if n > 1:
factors.add(... | 2 |
import unittest
from transformers import is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
... | 2 | 1 |
from .integrations import (
is_optuna_available,
is_ray_available,
is_sigopt_available,
is_wandb_available,
run_hp_search_optuna,
run_hp_search_ray,
run_hp_search_sigopt,
run_hp_search_wandb,
)
from .trainer_utils import (
HPSearchBackend,
default_hp_space_opt... | 2 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transfo... | 2 | 1 |
import pytest
UpperCAmelCase_ = """__dummy_dataset1__"""
UpperCAmelCase_ = """
import json
import os
import datasets
REPO_URL = \"https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/\"
URLS = {\"train\": REPO_URL + \"wikiann-bn-train.jsonl\", \"validation\": REPO_... | 2 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = {"""openai-gpt""": """https://huggingface.co/openai-gpt/resolve/main/config.json"""}
class lowerCamelCase__ ( _A):
"""simple ... | 2 | 1 |
import collections
import inspect
import unittest
from typing import Dict, List, Tuple
from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_available
from ...test_backbone_comm... | 2 |
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_in... | 2 | 1 |
import gc
import unittest
import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
ClapTextConfig,
ClapTextModelWithProjection,
RobertaTokenizer,
SpeechTaHifiGan,
SpeechTaHifiGanConfig,
)
from diffusers import (
AudioLDMPipeline,
Au... | 2 |
UpperCAmelCase_ = 0 # The first color of the flag.
UpperCAmelCase_ = 1 # The second color of the flag.
UpperCAmelCase_ = 2 # The third color of the flag.
UpperCAmelCase_ = (red, white, blue)
def SCREAMING_SNAKE_CASE_ ( _snake_case :list ) -> list:
if not seque... | 2 | 1 |
def SCREAMING_SNAKE_CASE_ ( _snake_case :List[Any] ) -> Tuple:
_A = 1
_A = 2
while i * i <= n:
_A = 0
while n % i == 0:
n //= i
multiplicity += 1
n_divisors *= multiplicity + 1
i += 1
if n > 1:
... | 2 |
import itertools
import math
def SCREAMING_SNAKE_CASE_ ( _snake_case :int ) -> bool:
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes... | 2 | 1 |
import string
def SCREAMING_SNAKE_CASE_ ( _snake_case :str ) -> None:
for key in range(len(string.ascii_uppercase ) ):
_A = ''''''
for symbol in message:
if symbol in string.ascii_uppercase:
_A = string.ascii_uppercase.find(_... | 2 |
import collections
import os
import re
from pathlib import Path
UpperCAmelCase_ = """src/transformers"""
# Matches is_xxx_available()
UpperCAmelCase_ = re.compile(r"""is\_([a-z_]*)_available()""")
# Catches a one-line _import_struct = {xxx}
UpperCAmelCase_ = re.compile(r"""^_im... | 2 | 1 |
import functools
import gc
import inspect
import torch
from .imports import is_npu_available, is_xpu_available
def SCREAMING_SNAKE_CASE_ ( *_snake_case :Optional[int] ) -> Optional[int]:
if not isinstance(_snake_case , _snake_case ):
_A = list(_snake_case ... | 2 |
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from .... | 2 | 1 |
from math import isqrt, loga
def SCREAMING_SNAKE_CASE_ ( _snake_case :int ) -> list[int]:
_A = [True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j in range(i**2 , _snake_case , _snake_case ):
... | 2 |
import requests
from bsa import BeautifulSoup
def SCREAMING_SNAKE_CASE_ ( _snake_case :str = "AAPL" ) -> str:
_A = F'''https://in.finance.yahoo.com/quote/{symbol}?s={symbol}'''
_A = BeautifulSoup(requests.get(_snake_case ).text , '''html.parser''' )
_... | 2 | 1 |
def SCREAMING_SNAKE_CASE_ ( _snake_case :int = 600_851_475_143 ) -> int:
try:
_A = int(_snake_case )
except (TypeError, ValueError):
raise TypeError('''Parameter n must be int or castable to int.''' )
if n <= 0:
raise ValueError('''Parameter n m... | 2 |
from graphs.minimum_spanning_tree_kruskal import kruskal
def SCREAMING_SNAKE_CASE_ ( ) -> Tuple:
_A = 9
_A = [
[0, 1, 4],
[0, 7, 8],
[1, 2, 8],
[7, 8, 7],
[7, 6, 1],
[2, 8, 2],
[8, 6, 6],
[2, 3, 7],
... | 2 | 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.tokenizat... | 2 |
def SCREAMING_SNAKE_CASE_ ( _snake_case :int ) -> int:
if not isinstance(_snake_case , _snake_case ):
raise ValueError('''Input must be an integer''' )
if input_num <= 0:
raise ValueError('''Input must be positive''' )
return sum(
divisor for divisor i... | 2 | 1 |
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import datasets
import numpy as np
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
EvalPrediction,
HfArgumentParser,
PreTrainedTokenizer,
TFA... | 2 |
UpperCAmelCase_ = 2_5_6
# Modulus to hash a string
UpperCAmelCase_ = 1_0_0_0_0_0_3
def SCREAMING_SNAKE_CASE_ ( _snake_case :str , _snake_case :str ) -> bool:
_A = len(_snake_case )
_A = len(_snake_case )
if p_len > t_len:
return Fa... | 2 | 1 |
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel
from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import HeunDiscreteS... | 2 |
import json
import os
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = {
"""vocab_file""": """vocab.json""",
"""tokenizer_config... | 2 | 1 |
from maths.prime_factors import prime_factors
def SCREAMING_SNAKE_CASE_ ( _snake_case :int ) -> int:
if not isinstance(_snake_case , _snake_case ):
_A = F'''Input value of [number={number}] must be an integer'''
raise TypeError(_snake_case )
if number <... | 2 |
from __future__ import annotations
from sys import maxsize
from typing import Generic, TypeVar
UpperCAmelCase_ = TypeVar("""T""")
def SCREAMING_SNAKE_CASE_ ( _snake_case :int ) -> int:
return (position - 1) // 2
def SCREAMING_SNAKE_CASE_ ( _snake_case :int ) -> ... | 2 | 1 |
import unittest
from transformers import LiltConfig, 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 ModelTest... | 2 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = """▁"... | 2 | 1 |
from __future__ import annotations
from typing import Any
class lowerCamelCase__ :
"""simple docstring"""
def __init__( self : List[str] , __lowerCAmelCase : int , __lowerCAmelCase : int , __lowerCAmelCase : float = 0 ) -> None:... | 2 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE_ ( _snake_case :dict , _snake_case :str ) -> set[str]:
_A , _A = set(_snake_case ), [start]
while stack:
_A = stack.pop()
explored.add(_snake_case )
# Differences from B... | 2 | 1 |
from __future__ import annotations
from collections.abc import Iterator
from typing import Generic, TypeVar
UpperCAmelCase_ = TypeVar("""T""")
class lowerCamelCase__ ( Generic[T]):
"""simple docstring"""
def __init__( self : Tuple , __lowerCAmelCase : ... | 2 |
from .configuration_bert_masked import MaskedBertConfig
from .modeling_bert_masked import (
MaskedBertForMultipleChoice,
MaskedBertForQuestionAnswering,
MaskedBertForSequenceClassification,
MaskedBertForTokenClassification,
MaskedBertModel,
)
from .modules import *
| 2 | 1 |
from collections.abc import Sequence
def SCREAMING_SNAKE_CASE_ ( _snake_case :Sequence[int] | None = None ) -> int:
if nums is None or not nums:
raise ValueError('''Input sequence should not be empty''' )
_A = nums[0]
for i in range(1 , len(_snake_case )... | 2 |
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = {
"""xlnet-base-cased""": """https://huggingface.co/xlnet-base-cased/resolve/main/config.json""",
"""xlnet-large-cas... | 2 | 1 |
import unittest
from transformers import is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
... | 2 |
def SCREAMING_SNAKE_CASE_ ( _snake_case :bytes ) -> str:
return "".join([hex(_snake_case )[2:].zfill(2 ).upper() for byte in list(_snake_case )] )
def SCREAMING_SNAKE_CASE_ ( _snake_case :str ) -> bytes:
# Check data validity, following RFC3548
# https://... | 2 | 1 |
import inspect
import unittest
from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_configurat... | 2 |
def SCREAMING_SNAKE_CASE_ ( _snake_case :list ) -> bool:
if not isinstance(_snake_case , _snake_case ):
raise ValueError('''Input series is not valid, valid series - [2, 4, 6]''' )
if len(_snake_case ) == 0:
raise ValueError('''Input list must be a non empty list'... | 2 | 1 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
# Register SEW's fairseq modules
from sew_asapp import tasks # noqa: F401
from transformers import (
SEWConfig,
SEWForCTC,
SEWModel,
WavaVecaCTCTokenizer,
WavaVecaFeature... | 2 |
import math
import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def SCREAMING_SNAKE_CASE_ ( _snake_case :int = 3 ) -> qiskit.result.counts.Counts:
if isinstance(_snake_case , _snake_case ):
raise TypeError(''... | 2 | 1 |
UpperCAmelCase_ = """Alexander Joslin"""
import operator as op
from .stack import Stack
def SCREAMING_SNAKE_CASE_ ( _snake_case :str ) -> int:
_A = {'''*''': op.mul, '''/''': op.truediv, '''+''': op.add, '''-''': op.sub}
_A = Stack()
_A = ... | 2 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
# Register SEW's fairseq modules
from sew_asapp import tasks # noqa: F401
from transformers import (
SEWConfig,
SEWForCTC,
SEWModel,
WavaVecaCTCTokenizer,
WavaVecaFeature... | 2 | 1 |
import logging
import math
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union
import torch
from .tensor_utils import tensor_tree_map, tree_map
def SCREAMING_SNAKE_CASE_ ( _snake_case :Union[dict, list, tuple, torch.Tensor] ... | 2 |
import unittest
from transformers import is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
... | 2 | 1 |
# flake8: noqa
# Lint as: python3
UpperCAmelCase_ = [
"""VerificationMode""",
"""Version""",
"""disable_progress_bar""",
"""enable_progress_bar""",
"""is_progress_bar_enabled""",
"""experimental""",
]
from .info_utils import VerificationMode
from .logging import disabl... | 2 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transfo... | 2 | 1 |
UpperCAmelCase_ = {
"""meter""": """m""",
"""kilometer""": """km""",
"""megametre""": """Mm""",
"""gigametre""": """Gm""",
"""terametre""": """Tm""",
"""petametre""": """Pm""",
"""exametre""": """Em""",
"""zettametre""": """Zm""",
"""yottametre""": """Ym""",
}... | 2 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = {"""openai-gpt""": """https://huggingface.co/openai-gpt/resolve/main/config.json"""}
class lowerCamelCase__ ( _A):
"""simple ... | 2 | 1 |
import os
from distutils.util import strtobool
def SCREAMING_SNAKE_CASE_ ( _snake_case :Any , _snake_case :List[Any] ) -> Optional[Any]:
for e in env_keys:
_A = int(os.environ.get(_snake_case , -1 ) )
if val >= 0:
return val
return default
... | 2 |
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_in... | 2 | 1 |
import argparse
import json
from pathlib import Path
import torch
import torchaudio
from datasets import load_dataset
from huggingface_hub import hf_hub_download
from transformers import ASTConfig, ASTFeatureExtractor, ASTForAudioClassification
from transformers.utils import logging
logging.set... | 2 |
UpperCAmelCase_ = 0 # The first color of the flag.
UpperCAmelCase_ = 1 # The second color of the flag.
UpperCAmelCase_ = 2 # The third color of the flag.
UpperCAmelCase_ = (red, white, blue)
def SCREAMING_SNAKE_CASE_ ( _snake_case :list ) -> list:
if not seque... | 2 | 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_in... | 2 |
import itertools
import math
def SCREAMING_SNAKE_CASE_ ( _snake_case :int ) -> bool:
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes... | 2 | 1 |
import argparse
import json
import os
import torch
from transformers.file_utils import has_file
from diffusers import UNetaDConditionModel, UNetaDModel
UpperCAmelCase_ = False
UpperCAmelCase_ = True
UpperCAmelCase_ = False
if __name__ == "__main__":
UpperCAmelCase_ =... | 2 |
import collections
import os
import re
from pathlib import Path
UpperCAmelCase_ = """src/transformers"""
# Matches is_xxx_available()
UpperCAmelCase_ = re.compile(r"""is\_([a-z_]*)_available()""")
# Catches a one-line _import_struct = {xxx}
UpperCAmelCase_ = re.compile(r"""^_im... | 2 | 1 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE_ ( _snake_case :int | str ) -> bool:
_A = str(_snake_case )
return n == n[::-1]
def SCREAMING_SNAKE_CASE_ ( _snake_case :int = 1_000_000 ) -> Any:
_A = 0
for i in range(1 , ... | 2 |
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from .... | 2 | 1 |
# XXX: we want transformers master here - in the absense of conftest manipulating sys.path:
# hack it in for now:
import sys
from pathlib import Path
UpperCAmelCase_ = Path(__file__).resolve().parents[3] / """src"""
sys.path.insert(1, str(git_repo_path))
import dataclasses # noqa
import io ... | 2 |
import requests
from bsa import BeautifulSoup
def SCREAMING_SNAKE_CASE_ ( _snake_case :str = "AAPL" ) -> str:
_A = F'''https://in.finance.yahoo.com/quote/{symbol}?s={symbol}'''
_A = BeautifulSoup(requests.get(_snake_case ).text , '''html.parser''' )
_... | 2 | 1 |
import argparse
import torch
from torch import nn
from transformers import MBartConfig, MBartForConditionalGeneration
def SCREAMING_SNAKE_CASE_ ( _snake_case :List[str] ) -> Optional[Any]:
_A = [
'''encoder.version''',
'''decoder.version''',
'''mod... | 2 |
from graphs.minimum_spanning_tree_kruskal import kruskal
def SCREAMING_SNAKE_CASE_ ( ) -> Tuple:
_A = 9
_A = [
[0, 1, 4],
[0, 7, 8],
[1, 2, 8],
[7, 8, 7],
[7, 6, 1],
[2, 8, 2],
[8, 6, 6],
[2, 3, 7],
... | 2 | 1 |
def SCREAMING_SNAKE_CASE_ ( _snake_case :list ) -> bool:
if not isinstance(_snake_case , _snake_case ):
raise ValueError('''Input series is not valid, valid series - [2, 4, 6]''' )
if len(_snake_case ) == 0:
raise ValueError('''Input list must be a non empty list'... | 2 |
def SCREAMING_SNAKE_CASE_ ( _snake_case :int ) -> int:
if not isinstance(_snake_case , _snake_case ):
raise ValueError('''Input must be an integer''' )
if input_num <= 0:
raise ValueError('''Input must be positive''' )
return sum(
divisor for divisor i... | 2 | 1 |
from graphs.minimum_spanning_tree_kruskal import kruskal
def SCREAMING_SNAKE_CASE_ ( ) -> Tuple:
_A = 9
_A = [
[0, 1, 4],
[0, 7, 8],
[1, 2, 8],
[7, 8, 7],
[7, 6, 1],
[2, 8, 2],
[8, 6, 6],
[2, 3, 7],
... | 2 |
UpperCAmelCase_ = 2_5_6
# Modulus to hash a string
UpperCAmelCase_ = 1_0_0_0_0_0_3
def SCREAMING_SNAKE_CASE_ ( _snake_case :str , _snake_case :str ) -> bool:
_A = len(_snake_case )
_A = len(_snake_case )
if p_len > t_len:
return Fa... | 2 | 1 |
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from .... | 2 |
import json
import os
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = {
"""vocab_file""": """vocab.json""",
"""tokenizer_config... | 2 | 1 |
def SCREAMING_SNAKE_CASE_ ( _snake_case :int ) -> int:
if not isinstance(_snake_case , _snake_case ):
raise ValueError('''Input must be an integer''' )
if input_num <= 0:
raise ValueError('''Input must be positive''' )
return sum(
divisor for divisor i... | 2 |
from __future__ import annotations
from sys import maxsize
from typing import Generic, TypeVar
UpperCAmelCase_ = TypeVar("""T""")
def SCREAMING_SNAKE_CASE_ ( _snake_case :int ) -> int:
return (position - 1) // 2
def SCREAMING_SNAKE_CASE_ ( _snake_case :int ) -> ... | 2 | 1 |
import warnings
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeat... | 2 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = """▁"... | 2 | 1 |
import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset
from ..utils.generic import ModelOutput
class lowerCamelCase__ ( _A):
"""simple docstring"""
def __init__( self : Tuple , __lowerCAmelCase : Optional[int] , __lower... | 2 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE_ ( _snake_case :dict , _snake_case :str ) -> set[str]:
_A , _A = set(_snake_case ), [start]
while stack:
_A = stack.pop()
explored.add(_snake_case )
# Differences from B... | 2 | 1 |
from functools import reduce
UpperCAmelCase_ = (
"""73167176531330624919225119674426574742355349194934"""
"""96983520312774506326239578318016984801869478851843"""
"""85861560789112949495459501737958331952853208805511"""
"""12540698747158523863050715693290963295227443043557"""
"... | 2 |
from .configuration_bert_masked import MaskedBertConfig
from .modeling_bert_masked import (
MaskedBertForMultipleChoice,
MaskedBertForQuestionAnswering,
MaskedBertForSequenceClassification,
MaskedBertForTokenClassification,
MaskedBertModel,
)
from .modules import *
| 2 | 1 |
import math
import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def SCREAMING_SNAKE_CASE_ ( _snake_case :int = 3 ) -> qiskit.result.counts.Counts:
if isinstance(_snake_case , _snake_case ):
raise TypeError(''... | 2 |
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = {
"""xlnet-base-cased""": """https://huggingface.co/xlnet-base-cased/resolve/main/config.json""",
"""xlnet-large-cas... | 2 | 1 |
# 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 ... | 2 |
def SCREAMING_SNAKE_CASE_ ( _snake_case :bytes ) -> str:
return "".join([hex(_snake_case )[2:].zfill(2 ).upper() for byte in list(_snake_case )] )
def SCREAMING_SNAKE_CASE_ ( _snake_case :str ) -> bytes:
# Check data validity, following RFC3548
# https://... | 2 | 1 |
UpperCAmelCase_ = 0 # The first color of the flag.
UpperCAmelCase_ = 1 # The second color of the flag.
UpperCAmelCase_ = 2 # The third color of the flag.
UpperCAmelCase_ = (red, white, blue)
def SCREAMING_SNAKE_CASE_ ( _snake_case :list ) -> list:
if not seque... | 2 |
def SCREAMING_SNAKE_CASE_ ( _snake_case :list ) -> bool:
if not isinstance(_snake_case , _snake_case ):
raise ValueError('''Input series is not valid, valid series - [2, 4, 6]''' )
if len(_snake_case ) == 0:
raise ValueError('''Input list must be a non empty list'... | 2 | 1 |
class lowerCamelCase__ :
"""simple docstring"""
def __init__( self : List[Any] , __lowerCAmelCase : list[int] ) -> None:
_A = len(__lowerCAmelCase )
_A = [0] * len_array
if len_array > 0:
_A = array[0]
... | 2 |
import math
import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def SCREAMING_SNAKE_CASE_ ( _snake_case :int = 3 ) -> qiskit.result.counts.Counts:
if isinstance(_snake_case , _snake_case ):
raise TypeError(''... | 2 | 1 |
import os
from pathlib import Path
def SCREAMING_SNAKE_CASE_ ( ) -> Dict:
from torch.utils.cpp_extension import load
_A = Path(_snake_case ).resolve().parent.parent.parent / '''kernels''' / '''deformable_detr'''
_A = [
root / filename
for fi... | 2 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
# Register SEW's fairseq modules
from sew_asapp import tasks # noqa: F401
from transformers import (
SEWConfig,
SEWForCTC,
SEWModel,
WavaVecaCTCTokenizer,
WavaVecaFeature... | 2 | 1 |
from __future__ import annotations
from typing import Any
class lowerCamelCase__ ( _A):
"""simple docstring"""
pass
class lowerCamelCase__ :
"""simple docstring"""
def __init__( self : int , __lowerCAmelCase : Any ) -> None:
_A ... | 2 |
import unittest
from transformers import is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
... | 2 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = {
"""microsoft/focalnet-tiny""": """htt... | 2 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transfo... | 2 | 1 |
import argparse
import torch
from huggingface_hub import hf_hub_download
from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging
logging.set_verbosity_info()
UpperCAmelCase_ = logging.get_logger(__name__)
def... | 2 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = {"""openai-gpt""": """https://huggingface.co/openai-gpt/resolve/main/config.json"""}
class lowerCamelCase__ ( _A):
"""simple ... | 2 | 1 |
from __future__ import annotations
from sys import maxsize
from typing import Generic, TypeVar
UpperCAmelCase_ = TypeVar("""T""")
def SCREAMING_SNAKE_CASE_ ( _snake_case :int ) -> int:
return (position - 1) // 2
def SCREAMING_SNAKE_CASE_ ( _snake_case :int ) -> ... | 2 |
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_in... | 2 | 1 |
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