code stringlengths 82 53.2k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
'''simple docstring'''
import logging
import os
from logging import (
CRITICAL, # NOQA
DEBUG, # NOQA
ERROR, # NOQA
FATAL, # NOQA
INFO, # NOQA
NOTSET, # NOQA
WARN, # NOQA
WARNING, # NOQA
)
from typing import Optional
from tqdm import auto as... | 116 |
'''simple docstring'''
import numpy as np
lowercase : str = [
["""a""", """b""", """c""", """d""", """e"""],
["""f""", """g""", """h""", """i""", """k"""],
["""l""", """m""", """n""", """o""", """p"""],
["""q""", """r""", """s""", """t""", """u"""],
... | 116 | 1 |
'''simple docstring'''
from __future__ import annotations
import math
def A_ ( snake_case , snake_case ):
SCREAMING_SNAKE_CASE:Optional[Any] = u
for i in range(1 , __UpperCAmelCase ):
SCREAMING_SNAKE_CASE:Dict = temp * (u - i)
return t... | 713 |
'''simple docstring'''
from math import factorial
def A_ ( snake_case , snake_case ):
# If either of the conditions are true, the function is being asked
# to calculate a factorial of a negative number, which is not possible
if n < k or k < 0:
raise ValueError("Please en... | 465 | 0 |
'''simple docstring'''
import unittest
from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available
from ...test_tokenization_common import TokenizerT... | 374 |
'''simple docstring'''
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, l... | 374 | 1 |
def _SCREAMING_SNAKE_CASE ( a ) -> int:
__A : list[list[int]] = [[0 for _ in range(a )] for _ in range(m + 1 )]
for i in range(m + 1 ):
__A : Optional[int] = 1
for n in range(m + 1 ):
for k in range(1 , a ):
... | 703 |
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
UpperCAmelCase : Dict = ''''''
UpperCAmelCase : Union[str, Any] = ''''''
UpperCAmelCase : Optional[int] = ''''''
UpperCAmelCase : Union[str, Any] = 1 # (0 is vert... | 77 | 0 |
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vis... | 144 |
from itertools import zip_longest
import requests
from bsa import BeautifulSoup
from pandas import DataFrame
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : str = "laptop" ) -> DataFrame:
UpperCAmelCase_ = f'https://www.amazon.in/laptop/s?k={product}'
UpperCA... | 144 | 1 |
'''simple docstring'''
from maths.prime_factors import prime_factors
def __UpperCAmelCase ( SCREAMING_SNAKE_CASE__: int ) -> int:
"""simple docstring"""
if not isinstance(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__ ):
__a = f"""Input value of... | 270 |
'''simple docstring'''
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_torch_available():
import torch... | 270 | 1 |
from __future__ import annotations
import unittest
from transformers import EsmConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_atten... | 655 |
# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import flax
import jax
import jax.numpy as jnp
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
... | 655 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCamelCase = logging.get_logger(__name__)
__lowerCamelCase = {
'edbeeching/decision-transformer-gym-hopper-medium': (
'https://huggingface.co/edbe... | 710 |
'''simple docstring'''
from __future__ import annotations
A = '#'
class __snake_case :
def __init__( self ):
"""simple docstring"""
lowerCamelCase : dict = {}
def UpperCAmelCase_ ( self, A ):
... | 449 | 0 |
from ....utils import logging
snake_case_ = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase ):
def __init__(self : Optional[int] , a__ : List[str] , a__ : List[Any]=None , a__ : Optional[int]=... | 592 |
# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position
snake_case_ = '2.13.1'
import platform
import pyarrow
from packaging import version
if version.parse(platform.python_version()) < version.parse('3.7... | 592 | 1 |
"""simple docstring"""
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 __A (snake_case__):
'''simple docstring'''
... | 2 |
"""simple docstring"""
from math import factorial
def _a ( _SCREAMING_SNAKE_CASE = 20 ) -> int:
snake_case_ = 2 * n # middle entry of odd rows starting at row 3 is the solution for n = 1,
# 2, 3,...
snake_case_ = n // 2
return int(fact... | 2 | 1 |
import logging
import random
import ray
from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex
A_ : str = logging.getLogger(__name__)
class _lowerCAmelCase:
"""si... | 57 |
'''simple docstring'''
import unittest
from transformers import AutoConfig, AutoTokenizer, BertConfig, TensorType, is_flax_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, slow
if is_flax_available():
import jax
from transformers.models.auto.modeling_flax_auto i... | 195 | 0 |
'''simple docstring'''
import string
import numpy
def _A ( A__ , A__ ):
"""simple docstring"""
return b if a == 0 else greatest_common_divisor(b % a , A__ )
class lowercase_ :
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = ... | 624 |
'''simple docstring'''
import argparse
import logging
from collections import namedtuple
import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer # The authors' implementation
from transformers import BertTokenizer
logging.basicConfig(level=logging.INFO)
lower... | 624 | 1 |
import json
import os
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__a = logging.get_logger(__name__)
__a = {
'vocab_file': 'vocab.json',
'tokenizer_config_file': 'tokenizer_config.json',... | 97 |
'''simple docstring'''
import argparse
import torch
from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def __a ( lowerCAmelCase__ : Union[str, Any] , lowerCAmelCase__ : ... | 688 | 0 |
def _a ( __lowercase ) -> int:
"""simple docstring"""
if a < 0:
raise ValueError('Input value must be a positive integer' )
elif isinstance(__lowercase , __lowercase ):
raise TypeError('Input value must be a \'int\' type' )
... | 567 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
'bigcode/gpt_bigcode-santacoder': 'https://huggingface.co/bigcode/gpt_bigcode-santacoder/resolve/main/config.json',
}
class lowerCAmelCase_ (... | 567 | 1 |
import gc
import unittest
from transformers import CTRLConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTeste... | 662 |
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
from ..... | 80 | 0 |
'''simple docstring'''
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 ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_feat... | 716 |
'''simple docstring'''
def lowerCAmelCase_ ( __A : List[str] ):
'''simple docstring'''
snake_case: str = [0] * len(__A )
snake_case: Tuple = []
snake_case: Tuple = [1] * len(__A )
for values in graph.values():
... | 692 | 0 |
"""simple docstring"""
from abc import ABC, abstractmethod
from typing import List, Optional
class lowerCAmelCase__ ( A_ ):
def __init__( self : Optional[int] ):
# test for the above condition
self.test()
def lowercase ... | 224 |
"""simple docstring"""
from __future__ import annotations
import math
class lowerCAmelCase__ :
def __init__( self : int , _lowerCamelCase : int ):
_snake_case = size
# approximate the overall size of segment tree with given valu... | 224 | 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 :Union[str, Any] = logging.get_logger(__name__)
__UpperCAm... | 709 |
'''simple docstring'''
import unittest
from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch
class a ( unittest.TestCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Any = JukeboxTokenizer
S... | 266 | 0 |
'''simple docstring'''
def UpperCAmelCase_ ( A ):
'''simple docstring'''
return str(A ) == str(A )[::-1]
def UpperCAmelCase_ ( A ):
'''simple docstring'''
return int(A ) + int(str(A )[::-1] )
def UpperCAmelCase_ ( A = 1_0_... | 120 |
'''simple docstring'''
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ : Any = logging.get_logger(__name__)
UpperCAmelCase_ : Any = {
"asapp/sew-tiny-100k": "https://huggingface.co/asapp/sew-... | 120 | 1 |
"""simple docstring"""
import tempfile
import unittest
import numpy as np
import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow
from ...generation.test_flax_utils import Fla... | 712 |
"""simple docstring"""
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> bool:
SCREAMING_SNAKE_CASE__ : Optional[Any] = len(__lowerCAmelCase ) + 1
SCREAMING_SNAKE_CASE__ : int = len(__lowerCAmelCase ) + 1
# dp is a 2d mat... | 12 | 0 |
from __future__ import annotations
import inspect
import unittest
from typing import List, Tuple
from transformers import RegNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configurat... | 25 |
'''simple docstring'''
from packaging import version
from .import_utils import is_accelerate_available
if is_accelerate_available():
import accelerate
def __lowercase (_lowercase ) -> Optional[Any]:
"""simple docstring"""
if not is_accelerate_available():
return method
__... | 150 | 0 |
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roberta import RobertaTokenizer
U... | 718 |
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... | 634 | 0 |
'''simple docstring'''
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' wh... | 24 |
'''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
if is_torch_... | 24 | 1 |
"""simple docstring"""
from numpy import exp, pi, sqrt
def lowercase (_snake_case ,_snake_case = 0.0 ,_snake_case = 1.0 ) -> int:
'''simple docstring'''
return 1 / sqrt(2 * pi * sigma**2 ) * exp(-((x - mu) ** 2) / (2 * sigma**2) )
if __name__ == "__main__":
import doctest
docte... | 228 |
"""simple docstring"""
from math import ceil
from typing import List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import BatchFeature, SequenceFeatureExtractor
from ...utils import TensorType, logging
_A ... | 228 | 1 |
'''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
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = OrderedD... | 173 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import is_flax_available
from transformers.testing_utils import require_flax
from ..test_modeling_flax_common import ids_tensor
if is_flax_available():
import jax
import jax.numpy as jnp
from transformer... | 50 | 0 |
"""simple docstring"""
from math import isqrt
def __UpperCamelCase ( SCREAMING_SNAKE_CASE ) -> bool:
"""simple docstring"""
return all(number % divisor != 0 for divisor in range(2 , isqrt(SCREAMING_SNAKE_CASE ) + 1 ) )
def __UpperCamelCase ( SCREAMING_SNAKE_C... | 614 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
_SCREAMING_SNAKE_CASE = {
"""configuration_mobilevit""": ["""MOBILEVIT_PRETRAINED_CONFIG_... | 614 | 1 |
"""simple docstring"""
import logging
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import arg_to_scheduler
from transformers import TrainingArguments
_lowercase = logging.getLogger(__name__)
@dataclass
class lowerCAmelCase_ ( _lowercase )... | 91 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase_ : List[Any] = logging.get_logger(__name__)
UpperCamelCase_ : Tuple = {
'''RWKV/rwkv-4-169m-pile''': '''https://huggingface.co/RWKV/rwkv-4-169m-pile/resol... | 185 | 0 |
import os
import numpy
import onnx
def __UpperCamelCase ( lowerCAmelCase__ : List[str] , lowerCAmelCase__ : Tuple ):
__a : Optional[Any] = a.name
__a : int = b.name
__a : Dict = ''''''
__a : ... | 714 |
from collections.abc import Callable
import numpy as np
def __UpperCamelCase ( lowerCAmelCase__ : Callable , lowerCAmelCase__ : float , lowerCAmelCase__ : float , lowerCAmelCase__ : float , lowerCAmelCase__ : ... | 326 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
UpperCamelCase_ = {
'''configuration_altclip''': [
'''ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''AltCLIPConfig''',
... | 209 | '''simple docstring'''
import absl # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import six # noqa:... | 209 | 1 |
def lowerCAmelCase__ ( a__ ) ->Optional[Any]:
'''simple docstring'''
if not isinstance(UpperCamelCase__ , UpperCamelCase__ ):
raise ValueError("multiplicative_persistence() only accepts integral values" )
if num < 0:
raise ValueError("multiplicative_persistence() does not a... | 719 | import argparse
import os
from io import BytesIO
from pathlib import Path
import requests
from clip_retrieval.clip_client import ClipClient
from PIL import Image
from tqdm import tqdm
def lowerCAmelCase__ ( a__ , a__ , a__ ) ->int:
'''simple docstring'''
_UpperCamelCase ... | 82 | 0 |
import argparse
import fairseq
import torch
from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging
logging.set_verbosity_info()
_a : List[Any] = logging.get_logger(__name__)
_a : Any = {
'post_extract_proj': 'feature_projection.projec... | 145 |
"""simple docstring"""
import argparse
import torch
from transformers import BertForMaskedLM
if __name__ == "__main__":
__snake_case : Optional[Any] = argparse.ArgumentParser(
description=(
'Extraction some layers of the full BertForMaskedLM or RObertaForMaske... | 293 | 0 |
"""simple docstring"""
from ...processing_utils import ProcessorMixin
class __lowerCAmelCase ( _UpperCamelCase):
'''simple docstring'''
__magic_name__ : Tuple = """WhisperFeatureExtractor"""
__magic_name__ : List[Any] = """WhisperTokenizer"""
... | 595 | """simple docstring"""
import collections
import os
from typing import List, Optional, Tuple
from transformers.utils import is_jieba_available, requires_backends
if is_jieba_available():
import jieba
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__A : Optional[int] ... | 595 | 1 |
import re
import subprocess
import sys
__lowerCamelCase : int = subprocess.check_output("""git merge-base main HEAD""".split()).decode("""utf-8""")
__lowerCamelCase : List[str] = subprocess.check_output(F"git diff --name-only {fork_point_sha}".split()).decode("""utf-8""").split()
... | 385 |
'''simple docstring'''
import argparse
import os
from pathlib import Path
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import ... | 158 | 0 |
'''simple docstring'''
def UpperCamelCase__ ( __magic_name__ : int ) -> int:
'''simple docstring'''
if not isinstance(__magic_name__ , __magic_name__ ):
raise TypeError("""Input value must be an 'int' type""" )
snake_case__ : List[str] = 0
whil... | 419 |
'''simple docstring'''
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, ... | 419 | 1 |
from __future__ import annotations
lowerCAmelCase_ = [-1_0, -5, 0, 5, 5.1, 1_1, 1_3, 2_1, 3, 4, -2_1, -1_0, -5, -1, 0]
lowerCAmelCase_ = [-5, 0, 5, 5.1, 1_1, 1_3, 2_1, -1, 4, -1, -1_0, -5, -1, 0, -1]
def lowerCamelCase_ ( _UpperCamelCase ) -> list[float]:
... | 60 |
from __future__ import annotations
from collections.abc import MutableSequence
class UpperCAmelCase_ :
"""simple docstring"""
def __init__( self: List[Any] , _UpperCAmelCase: int , _UpperCAmelCase: MutableSequence[float] ):
if len(_UpperCAmelCase ) != degree... | 687 | 0 |
import inspect
import unittest
from transformers import MobileViTConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...tes... | 60 |
import unittest
import numpy as np
from datasets import load_dataset
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is... | 60 | 1 |
import qiskit
def SCREAMING_SNAKE_CASE__ ( lowercase ,lowercase ) -> qiskit.result.counts.Counts:
snake_case : Optional[int] = qiskit.Aer.get_backend("""aer_simulator""" )
snake_case : Any = qiskit.QuantumCircuit(4 ,2 )
# encode inputs in qubits 0 a... | 587 |
from typing import List, Optional, Union
import numpy as np
import PIL.Image
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
PILImageResampling,
get_image... | 587 | 1 |
from collections.abc import Generator
from math import sin
def a ( _UpperCAmelCase : bytes ):
'''simple docstring'''
if len(_UpperCAmelCase ) != 32:
raise ValueError('''Input must be of length 32''' )
__UpperCAmelCase : Optional[Any] = ... | 704 |
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 tensorflow as tf
from transformers import TF... | 241 | 0 |
import os
import pytest
from attr import dataclass
__A : int = "us-east-1" # defaults region
@dataclass
class lowerCamelCase:
'''simple docstring'''
__magic_name__ = 42
__magic_name__ = 'arn:aws:iam::558105141721:role/sagemaker_e... | 27 |
'''simple docstring'''
from math import factorial
UpperCamelCase_ = {str(digit): factorial(digit) for digit in range(10)}
def _UpperCAmelCase ( _lowerCamelCase : int ) -> int:
if not isinstance(_lowerCamelCase , _lowerCamelCase ):
raise TypeError("""Parameter number... | 384 | 0 |
import unittest
from parameterized import parameterized
from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import Con... | 703 |
import pickle
import unittest
import torch
from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu
@require_cpu
class _lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def UpperCA... | 408 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
UpperCAmelCase = {
"""configuration_squeezebert""": [
"""SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""Sq... | 88 | from __future__ import annotations
import string
from itertools import cycle, product
from pathlib import Path
_lowercase : str =(
string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
_lowercase : list[int] =[ord(letter) for letter in string.ascii_lowercas... | 305 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModel,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableUn... | 708 |
def UpperCamelCase_ ( __a ) -> bool:
if num < 0:
return False
a__ : int = num
a__ : int = 0
while num > 0:
a__ : str = rev_num * 10 + (num % 10)
num //= 10
return num_copy == rev_num
if __... | 151 | 0 |
'''simple docstring'''
import sacrebleu as scb
from packaging import version
from sacrebleu import TER
import datasets
UpperCAmelCase : int = '\\n@inproceedings{snover-etal-2006-study,\n title = "A Study of Translation Edit Rate with Targeted Human Annotation",\n author = "Snover, Matthew and... | 627 |
'''simple docstring'''
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class lowerCAmelCase__ ( ... | 627 | 1 |
'''simple docstring'''
# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile
from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version import... | 265 |
'''simple docstring'''
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, ClassLabel, Features
from .base import TaskTemplate
@dataclass(frozen=SCREAMING_SNAKE_CASE )
class _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE ):
'''simpl... | 265 | 1 |
'''simple docstring'''
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class snake_case__ ( UpperCamelCase , UpperCamelCase):
@register_to_config
def __init__( self : Optional[Any] ,... | 541 |
'''simple docstring'''
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... | 541 | 1 |
"""simple docstring"""
from importlib import import_module
from .logging import get_logger
lowerCamelCase : List[Any] =get_logger(__name__)
class __snake_case:
'''simple docstring'''
def __init__( self , __lowerCamelCase , ... | 720 | """simple docstring"""
from __future__ import annotations
def _lowercase ( _SCREAMING_SNAKE_CASE : float , _SCREAMING_SNAKE_CASE : float , _SCREAMING_SNAKE_CASE : float , ) -> tuple[str, float]:
''... | 237 | 0 |
'''simple docstring'''
import platform
from argparse import ArgumentParser
import huggingface_hub
from .. import __version__ as version
from ..utils import is_accelerate_available, is_torch_available, is_transformers_available, is_xformers_available
from . import BaseDiffusersCLICommand
def __a ... | 185 | 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 floats_tensor, ids_te... | 604 | 0 |
"""simple docstring"""
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, ClassLabel, Features
from .base import TaskTemplate
@dataclass(frozen=lowerCAmelCase )
class SCREAMING_SNAKE_CASE ( lowerCAmelCase ):
'''simple docstring''... | 158 |
"""simple docstring"""
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, prepa... | 158 | 1 |
def A__ ( snake_case_ : Tuple , snake_case_ : Union[str, Any] , snake_case_ : str=False ):
if isinstance(snake_case_ , snake_case_ ) and isinstance(snake_case_ , snake_case_ ):
SCREAMING_SNAKE_CASE__: int= len(set_a.intersection(snake_case_ ) ... | 64 |
import argparse
import pathlib
import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version
from transformers import XLMRobertaConfig, XLMRobertaXLForMaskedLM, XLMRobertaXLFor... | 306 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections import deque
class UpperCAmelCase_ :
"""simple docstring"""
def __init__( self , UpperCAmelCase_ ):
snake_case_ = []
self.adlist.append(
{"value": "", "next_states": [], "fail_state":... | 420 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ = logging.get_logger(__name__)
lowercase__ = {
'''EleutherAI/gpt-neox-20b''': '''https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json''',
... | 420 | 1 |
"""simple docstring"""
from json import JSONDecodeError # Workaround for requests.exceptions.JSONDecodeError
import requests
def snake_case ( A__ = "isbn/0140328726" ):
UpperCAmelCase_ : int = olid.strip().strip("/" ) # Remove leading/trailing whitespace & slashes
if new_olid.count... | 95 |
'''simple docstring'''
import socket
def lowerCAmelCase__ ( ):
_A : Dict = socket.socket(socket.AF_INET ,socket.SOCK_STREAM )
_A : List[Any] = socket.gethostname()
_A : List[str] = 12312
sock.connect((host, port... | 128 | 0 |
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
ConditionalDetrConfig,
ConditionalDetrForObjectDetection,
ConditionalDetrForSegmentation,
... | 675 |
def __SCREAMING_SNAKE_CASE ( ) -> list[list[int]]:
'''simple docstring'''
return [list(range(1000 - i , -1000 - i , -1 ) ) for i in range(1000 )]
lowerCAmelCase = generate_large_matrix()
lowerCAmelCase = (
[[4, 3, 2, -1], [3, 2,... | 675 | 1 |
'''simple docstring'''
import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor
def lowerCAmelCase_ ( snake_case_ : dict... | 78 |
"""simple docstring"""
import dataclasses
import json
import sys
import types
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, ArgumentTypeError
from copy import copy
from enum import Enum
from inspect import isclass
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, ... | 156 | 0 |
"""simple docstring"""
import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path
import timm
import torch
from huggingface_hub import hf_hub_download
from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImageProc... | 173 | """simple docstring"""
def UpperCamelCase ( _lowerCAmelCase : Tuple , _lowerCAmelCase : Union[str, Any] ):
# "extended trapezoidal rule"
# int(f) = dx/2 * (f1 + 2f2 + ... + fn)
__a = (boundary[1] - boundary[0]) / steps
__a = boundary[0]
__a = ... | 173 | 1 |
def A ( _lowercase ):
SCREAMING_SNAKE_CASE : int = 1
SCREAMING_SNAKE_CASE : Dict = 2
while i * i <= n:
SCREAMING_SNAKE_CASE : Optional[int] = 0
while n % i == 0:
n //= i
... | 248 | def A ( _lowercase = 10**9 ):
SCREAMING_SNAKE_CASE : int = 1
SCREAMING_SNAKE_CASE : str = 2
SCREAMING_SNAKE_CASE : List[Any] = 0
SCREAMING_SNAKE_CASE : int = 0
SCREAMING_SNAKE_CA... | 248 | 1 |
"""simple docstring"""
from collections import UserDict
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... | 361 |
"""simple docstring"""
from __future__ import annotations
from collections import namedtuple
def lowercase_ ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
"""simple docstring"""
A_ : int = namedtuple('''result''' , '''name value''' )... | 361 | 1 |
from collections import deque
class a__ :
def __init__( self : List[str],_A : str,_A : int,_A : int ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[Any] = process_name # process name
SCREAMING_SNAKE_CASE_ : ... | 216 | 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 import CORPUS_NAME,... | 216 | 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,
resize,... | 491 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import DebertaVaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin,... | 491 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
lowerCAmelCase__ = {'configuration_fnet': ['FNET_PRETRAINED_CONFIG_ARCHIVE_MAP',... | 621 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.util... | 621 | 1 |
"""simple docstring"""
from typing import List
import jiwer
import jiwer.transforms as tr
from packaging import version
import datasets
from datasets.config import PY_VERSION
if PY_VERSION < version.parse("""3.8"""):
import importlib_metadata
else:
import importlib.metadata as importlib_metadata
__snake... | 117 |
"""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_seed
from acceler... | 117 | 1 |
def UpperCamelCase ( _UpperCAmelCase : str ) -> list:
'''simple docstring'''
return [
txt[:a] + txt[a].upper() + txt[a + 1 :]
for a in range(len(_UpperCAmelCase ) )
if txt[a].isalpha()
]
if __name__ == "__main__":
__import__("""docte... | 461 |
from dataclasses import dataclass, field
from typing import Optional
@dataclass
class __lowercase :
_A = field(
default="codeparrot/codeparrot" , metadata={"help": "Model name or path of model to be trained."} )
_A = field(
default="./" , metadata={"help": "Save dir whe... | 461 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__magic_name__ : int = {
'''configuration_roberta_prelayernorm''': [
'''ROBERTA... | 602 |
'''simple docstring'''
import argparse
import math
import traceback
import dateutil.parser as date_parser
import requests
def A__ ( A_ ) -> str:
_lowercase = {}
_lowercase = job["started_at"]
_lowercase = job["completed_at"]
_lowercase = date_parser.... | 602 | 1 |
lowercase_: Optional[int] = '0.18.2'
from .configuration_utils import ConfigMixin
from .utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_inflect_available,
is_invisible_watermark_available,
is_k_diffusion_available,
is_k_diffusion_version,
... | 648 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Iterator
from typing import Generic, TypeVar
lowercase__ :Tuple = TypeVar('T')
class snake_case ( Generic[T] ):
'''simple docstring'''
def __ini... | 522 | 0 |
"""simple docstring"""
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when s... | 19 |
"""simple docstring"""
import itertools
import os
from collections import Counter, defaultdict
from concurrent.futures import ThreadPoolExecutor, as_completed
import numpy as np
import datasets
from .execute import check_correctness
a : Optional[Any] = '''\
@misc{chen2021evaluating,
title={Eva... | 19 | 1 |
lowerCAmelCase : Dict = {
'''A''': ['''B''', '''C''', '''E'''],
'''B''': ['''A''', '''D''', '''E'''],
'''C''': ['''A''', '''F''', '''G'''],
'''D''': ['''B'''],
'''E''': ['''A''', '''B''', '''D'''],
'''F''': ['''C'''],
'''G''': ['''C'''],
}
def A_ ( _UpperCAm... | 671 |
from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder
# @@protoc_insertion_point(imports)
lowerCAmelCase : Union[str, Any] ... | 214 | 0 |
import re
import tempfile
from pathlib import Path
import pytest
import yaml
from datasets.utils.readme import ReadMe
# @pytest.fixture
# def example_yaml_structure():
_lowerCamelCase = yaml.safe_load(
'''\
name: ""
allow_empty: false
allow_empty_text: true
subsections:
- name: "Dataset Card fo... | 704 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
_lowerCamelCase = {
'''configuration_roberta_prelayernorm''': [
'''ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIV... | 321 | 0 |
from math import ceil
def a_ ( lowerCAmelCase_ : Tuple, lowerCAmelCase_ : Dict ):
__lowerCAmelCase = list(range(0, lowerCAmelCase_ ) )
__lowerCAmelCase = [item for sublist in list(device_map.values() ) for item in sublist]
... | 53 | '''simple docstring'''
import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def _SCREAMING_SNAKE_CASE ( ):
with offline(OfflineSimulationMode.CONNECTION_TIMES_O... | 107 | 0 |
import collections
import importlib.util
import os
import re
from pathlib import Path
snake_case = "src/transformers"
# Matches is_xxx_available()
snake_case = re.compile(r"is\_([a-z_]*)_available()")
# Catches a one-line _import_struct = {xxx}
snake_case = re.compile(r"^_import_structure\s+=\s... | 587 | import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import (
AutoProcessor,
BertToken... | 587 | 1 |
class __magic_name__ :
def __init__( self : Optional[Any] ) -> int:
'''simple docstring'''
UpperCAmelCase = ''
UpperCAmelCase = ''
UpperCAmelCase = []
def SCREAMING_SNAKE_CASE_ ( self : List[str] , Up... | 323 |
'''simple docstring'''
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_commo... | 446 | 0 |
'''simple docstring'''
# 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
#
# U... | 339 |
'''simple docstring'''
from __future__ import annotations
from numpy import array, cos, cross, floataa, radians, sin
from numpy.typing import NDArray
def lowercase__ ( __UpperCamelCase : float , __UpperCamelCase : float , __UpperCamelCase : bool = False ):
... | 339 | 1 |
"""simple docstring"""
from collections.abc import Iterable
from typing import Generic, TypeVar
A_ = TypeVar("""_T""")
class __lowerCamelCase ( Generic[_T] ):
def __init__( self , UpperCAmelCase = None ):
lowerCamelCase_ = list(iterable or [] )
lowe... | 29 | def A ( _lowercase , _lowercase ):
return int((input_a, input_a).count(0 ) == 0 )
def A ( ):
assert and_gate(0 , 0 ) == 0
assert and_gate(0 , 1 ) == 0
assert and_gate(1 , 0 ) == 0
assert and_gate(1 , 1 ) == 1
... | 248 | 0 |
import numpy as np
import qiskit
def __SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE = 8 , SCREAMING_SNAKE_CASE = None ) -> str:
SCREAMING_SNAKE_CASE_ : List[Any] = np.random.default_rng(seed=SCREAMING_SNAKE_CASE_ )
# Roughly 25% of the qubits will contribute to the key.
... | 700 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
lowerCAmelCase__: Dict = {
"configuration_gpt_neo": ["GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoConfig", "GPTNeoOnnxConfig"],
}
try:
if no... | 311 | 0 |
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 .... | 141 |
"""simple docstring"""
import unittest
from transformers import SPIECE_UNDERLINE
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.tokenization_utils import AddedToken
from ...test... | 553 | 0 |
'''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 sepia as... | 493 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_snake_case : int = {
"""configuration_longformer""": [
"""LONGFORMER_PRETRAI... | 493 | 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(__na... | 104 |
import math
import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def UpperCamelCase_( snake_case__: int = 3 ) -> qiskit.result.counts.Counts:
if isinstance(snake_case__ , snake_case__ ):
raise TypeError('number of q... | 146 | 0 |
"""simple docstring"""
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
from .record_evaluation import evaluate as evaluate_record
lowercase__ = "\\n@article{wang2019superglue,\n title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems... | 717 |
"""simple docstring"""
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert_fast import BertTokenizerFast
from .tokenization_dpr ... | 63 | 0 |
"""simple docstring"""
from math import factorial
def __a ( A = 100 ) -> int:
'''simple docstring'''
return sum(int(A ) for x in str(factorial(A ) ) )
if __name__ == "__main__":
print(solution(int(input("""Enter the Number: """).strip()))) | 337 |
"""simple docstring"""
def __a ( A ) -> List[str]:
'''simple docstring'''
A__ = [0] * len(A )
A__ = []
A__ = []
A__ = 0
for values in graph.values():
for i in values:
indegree[i] += 1
for... | 337 | 1 |
"""simple docstring"""
import unittest
from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor... | 194 |
"""simple docstring"""
from typing import List, Optional, Union
import torch
from transformers import (
XLMRobertaTokenizer,
)
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...s... | 194 | 1 |
_lowercase = '0.18.2'
from .configuration_utils import ConfigMixin
from .utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_inflect_available,
is_invisible_watermark_available,
is_k_diffusion_available,
is_k_diffusion_version,
is_librosa_available,
... | 306 |
import math
import time
from typing import Dict, List, Optional
from torch.utils.data import Dataset
from transformers import SeqaSeqTrainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
import tor... | 306 | 1 |
'''simple docstring'''
def a_ ( __UpperCAmelCase ) -> bool:
"""simple docstring"""
snake_case: set[int] =set()
# To detect a back edge, keep track of vertices currently in the recursion stack
snake_case: set[int] =set()
... | 347 |
'''simple docstring'''
def a_ ( __UpperCAmelCase ) -> int:
"""simple docstring"""
if not isinstance(__UpperCAmelCase , __UpperCAmelCase ):
snake_case: Any =f'''Input value of [number={number}] must be an integer'''
... | 347 | 1 |
'''simple docstring'''
import unittest
from transformers import (
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TextClassificationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, s... | 210 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_lowercase : List[Any] = logging.get_logger(__name__)
_lowercase : Union[str, Any] ... | 210 | 1 |
"""simple docstring"""
from __future__ import annotations
from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any
@dataclass
class SCREAMING_SNAKE_CASE__ :
_lowerCAmelCase = 42
_lowerCAmelCase ... | 63 |
"""simple docstring"""
import math
from enum import Enum
from typing import Optional, Union
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from .utils import logging
lowercase__ = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE__ ( __... | 63 | 1 |
"""simple docstring"""
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenizat... | 626 |
"""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 tensorflow as tf
from transfor... | 626 | 1 |
"""simple docstring"""
from collections import deque
class snake_case :
"""simple docstring"""
def __init__( self : str ,lowerCamelCase__ : Any ,lowerCamelCase__ : str ,lowerCamelCase__ : List[str] ):
UpperCAmelCase__ = proces... | 707 | """simple docstring"""
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device
lowerCAmelCase__ : Optional[int] = False
class snake_case ( ... | 632 | 0 |
import unittest
import numpy as np
from transformers import MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING, TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
from transformers.pipelines import AudioClassificationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
... | 113 |
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from huggingface_hub import HfFolder, Repository, create_repo, delete_repo
from requests.exceptions import HTTPError
import transformers
from transformers import (
CONFIG_MAPPING,
FEATURE_EX... | 113 | 1 |
def UpperCamelCase ( _a = 1_0_0_0_0_0_0 ) -> int:
'''simple docstring'''
lowercase_ :Tuple = limit + 1
lowercase_ :Dict = [0] * limit
for first_term in range(1 , _a ):
for n in range(_a , _a , ... | 441 |
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 TFModelTesterMixin, ids_tensor... | 441 | 1 |
'''simple docstring'''
def A_ ( _lowerCAmelCase : int ):
"""simple docstring"""
if upper_limit < 0:
raise ValueError("Limit for the Catalan sequence must be ≥ 0" )
_lowerCamelCase : Optional[int] = [0] * (upper_limit + 1)
# Base case: C... | 44 |
"""simple docstring"""
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.activations import gelu_new, gelu_python, get_activation
@require_torch
class lowerCAmelCase__ ... | 512 | 0 |
"""simple docstring"""
# 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
im... | 702 |
"""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,
)
UpperCAmelCase = {
'''configuration... | 475 | 0 |
"""simple docstring"""
import json
import os
import tempfile
import datasets
from utils import generate_example_dataset, get_duration
lowercase__ = 50000
lowercase__ = 5000
lowercase__ , lowercase__ = os.path.split(__file__)
lowercase__ = os.path.join(RESULTS_BASEPATH,... | 581 |
"""simple docstring"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, TensorType... | 581 | 1 |
a__ : List[str] = '''Input must be a string of 8 numbers plus letter'''
a__ : int = '''TRWAGMYFPDXBNJZSQVHLCKE'''
def UpperCAmelCase_( a__ ):
"""simple docstring"""
if not isinstance(a__ , a__ ):
SCREAMING_SNAKE_CASE : ... | 333 |
from numpy import exp, pi, sqrt
def UpperCAmelCase_( a__ , a__ = 0.0 , a__ = 1.0 ):
"""simple docstring"""
return 1 / sqrt(2 * pi * sigma**2 ) * exp(-((x - mu) ** 2) / (2 * sigma**2) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 333 | 1 |
from argparse import ArgumentParser
from .env import EnvironmentCommand
def A ( ) -> Optional[Any]:
lowerCamelCase : List[Any] = ArgumentParser("Diffusers CLI tool" ,usage="diffusers-cli <command> [<args>]" )
lowerCamelCase : str = parser.add_s... | 311 |
from manim import *
class UpperCamelCase__ (lowerCAmelCase__ ):
'''simple docstring'''
def _lowercase ( self ) -> List[Any]:
lowerCamelCase : Any = Rectangle(height=0.5 , width=0.5 )
lowerCamelCase : Optional[Any] ... | 311 | 1 |
"""simple docstring"""
import math
class __lowerCAmelCase :
'''simple docstring'''
def __UpperCAmelCase ( self , _a , _a ):
__a = 0.0
__a = 0.0
for i in range(len(_a ) ):
da += math.pow((sample[i] - weights[0... | 65 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"unc-nlp/lxmert-base-uncased": "https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/config.json",
... | 65 | 1 |
import tempfile
import torch
from diffusers import PNDMScheduler
from .test_schedulers import SchedulerCommonTest
class _A ( a_ ):
SCREAMING_SNAKE_CASE_ : str =(PNDMScheduler,)
SCREAMING_SNAKE_CASE_ : str =(("num_inference_steps", 50),)
def _a... | 415 |
import os
# Precomputes a list of the 100 first triangular numbers
lowerCamelCase : str = [int(0.5 * n * (n + 1)) for n in range(1, 101)]
def __lowerCAmelCase ( ):
__lowerCAmelCase = os.path.dirname(os.path.realpath(__snake_case ) )
__lower... | 367 | 0 |
"""simple docstring"""
from datetime import datetime as dt
import os
from github import Github
__UpperCAmelCase = [
'good first issue',
'good second issue',
'good difficult issue',
'feature request',
'new model',
'wip',
]
def lowerCAmelCase ( ):
'''simple docstring'''
... | 721 |
"""simple docstring"""
import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils... | 194 | 0 |
import qiskit
def _SCREAMING_SNAKE_CASE ( lowercase : int , lowercase : int ):
'''simple docstring'''
lowerCamelCase_ = qiskit.Aer.get_backend('aer_simulator' )
# Create a Quantum Circuit acting on the q register
lowerCam... | 70 |
'''simple docstring'''
from __future__ import annotations
def lowerCamelCase_ ( __UpperCamelCase : list , __UpperCamelCase : int | None = None , __UpperCamelCase : int | None = None ) -> None:
"""simple docstring"""
if start i... | 292 | 0 |
"""simple docstring"""
def __UpperCamelCase ( snake_case__ , snake_case__ ):
_validate_point(snake_case__ )
_validate_point(snake_case__ )
if len(snake_case__ ) != len(snake_case__ ):
raise ValueError("""Both points must be in the same n-dimensional space""" )
return float(... | 700 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2... | 480 | 0 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging
if TYPE_CHECKING:
from ...feature_extraction_utils import F... | 1 | """simple docstring"""
import inspect
import unittest
from typing import List
import numpy as np
from transformers import EfficientFormerConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...t... | 425 | 0 |
'''simple docstring'''
def snake_case__ ( UpperCAmelCase : str ):
return "".join(chr(ord(UpperCAmelCase ) - 3_2 ) if "a" <= char <= "z" else char for char in word )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 716 |
from __future__ import annotations
import math
from collections.abc import Callable
def snake_case__ ( UpperCAmelCase : Callable[[int | float], int | float] , UpperCAmelCase : int | float , UpperCAmelCase : int | float , UpperCAmelCase : int = 1_0_0 ... | 111 | 0 |
'''simple docstring'''
import requests
A_ = "" # <-- Put your OpenWeatherMap appid here!
A_ = "https://api.openweathermap.org/data/2.5/"
def UpperCamelCase__ ( __SCREAMING_SNAKE_CASE = "Chicago" , __SCREAMING_SNAKE_CASE = APPID ) -> Optional[Any]:
return ... | 270 | # Copyright 2022 The HuggingFace Team and The OpenBMB 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
#
#... | 166 | 0 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import is_flaky, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image... | 721 |
'''simple docstring'''
import argparse
import os
import gluonnlp as nlp
import mxnet as mx
import numpy as np
import torch
from gluonnlp.base import get_home_dir
from gluonnlp.model.bert import BERTEncoder
from gluonnlp.model.utils import _load_vocab
from gluonnlp.vocab import Vocab
from packaging import version... | 555 | 0 |
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