code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
import re
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class __lowerCAmelCase ( __magic_name__ ):
"""simple docstring"""
snake_case_ = ['''image_processor''', '''tokenizer''']
snake_case... | 90 |
from __future__ import annotations
from collections import namedtuple
from dataclasses import dataclass
@dataclass
class __snake_case :
_a : int
_a : TreeNode | None= None
_a : TreeNode | None= None
lowercase : Dict = namedtuple("""CoinsDistribResult""",... | 20 | 0 |
import multiprocessing
import time
from arguments import PretokenizationArguments
from datasets import load_dataset
from transformers import AutoTokenizer, HfArgumentParser
def A_ ( A__ ) -> List[str]:
a__ : List[Any] = {}
a__ : Optional[Any] = tokenizer(ex... | 225 |
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... | 225 | 1 |
import pyarrow.parquet as pq
import pytest
from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size
from ..utils import assert_arrow... | 87 | 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 snake_case_ ( __A ):
__A :... | 87 | 1 |
from typing import Optional, Union
import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention
from ...modeling_utils import PreT... | 369 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, Features, Value
from .base import TaskTemplate
@dataclass(frozen=lowerCAmelCase )
class _lowercase ( lowerCAmelCase ):
"""simple docstring"""
__A = ... | 71 | 0 |
'''simple docstring'''
from __future__ import annotations
def snake_case_ ( lowerCAmelCase_ )-> int:
'''simple docstring'''
if not nums:
return 0
_UpperCAmelCase : Optional[Any] = nums[0]
_UpperCAmelCase : Dict = 0
... | 215 |
def __A ( __lowerCamelCase , __lowerCamelCase ) -> str:
if not isinstance(__lowerCamelCase , __lowerCamelCase ):
raise ValueError("""iterations must be defined as integers""" )
if not isinstance(__lowerCamelCase , __lowerCamelCase ) or not number >= 1:
... | 228 | 0 |
'''simple docstring'''
def _a( UpperCamelCase__ : Optional[Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : List[str] =len(UpperCamelCase__ )
while cur > 1:
# Find the maximum number in arr
SCREAMING_SNAKE_CASE__ ... | 364 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ = {
'configuration_megatron_bert': ['MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MegatronBertConfig'],
}
try:
if not is_torch_availabl... | 222 | 0 |
import copy
import re
class __lowerCamelCase :
"""simple docstring"""
UpperCamelCase__ = "hp"
UpperCamelCase__ = {}
UpperCamelCase__ = None
@classmethod
def UpperCamelCase ( cls , UpperCAmelCase ... | 39 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_a = logging.get_logger(__name__)
_a = {
'''distilbert-base-uncased''': '''https://huggingface.co/... | 39 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__lowerCamelCase = {'configuration_focalnet': ['FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FocalNetConfig']}
try:
... | 366 |
'''simple docstring'''
import argparse
import glob
import logging
import os
import sys
import time
from collections import defaultdict
from pathlib import Path
from typing import Dict, List, Tuple
import numpy as np
import pytorch_lightning as pl
import torch
from callbacks import SeqaSeqLoggingCallback, get_... | 101 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_lowerCamelCase : int = logging.get_logger(__name__)
_lowerCamelCase : List[A... | 28 |
"""simple docstring"""
# Logistic Regression from scratch
# In[62]:
# In[63]:
# importing all the required libraries
import numpy as np
from matplotlib import pyplot as plt
from sklearn import datasets
def _UpperCAmelCase ( __lowerCamelCase : str ) -> List[Any]:
ret... | 288 | 0 |
'''simple docstring'''
import flax.linen as nn
import jax.numpy as jnp
from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD
class lowercase ( nn.Module ):
"""simple docstring"""
UpperCAmelCase = 42
Uppe... | 369 |
'''simple docstring'''
from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available
if is_datasets_available():
import datasets
class ... | 349 | 0 |
"""simple docstring"""
import argparse
import re
from typing import Dict
import torch
from datasets import Audio, Dataset, load_dataset, load_metric
from transformers import AutoFeatureExtractor, pipeline
def lowercase ( lowerCAmelCase__ : Dataset , lowerCAmelCase__ :... | 45 |
"""simple docstring"""
def lowercase ( lowerCAmelCase__ : str = "The quick brown fox jumps over the lazy dog" , ) -> bool:
__a = set()
# Replace all the whitespace in our sentence
__a = input_str.replace(''' ''' , '''''' )
for a... | 45 | 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=UpperCamelCase_ )
class lowercase ( UpperCamelCase_ ):
lowercase_ : str =field(default='''automatic-speech-rec... | 362 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowercase__ :Optional[int] = logging.get_logger(__name__)
lowercase__ :List[Any] = {
"camembert-base": "https://huggingfac... | 97 | 0 |
"""simple docstring"""
import warnings
from typing import Dict
import numpy as np
from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline
if is_tf_available():
from ..models.auto.modeling_tf_auto import... | 46 |
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 impor... | 228 | 0 |
"""simple docstring"""
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis
from lavis.models import load_model_and_preprocess
from PIL import Image
from transformer... | 354 |
"""simple docstring"""
import importlib
import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel
def __lowerCAmelCase ( lowercase : Union[str, Any] , lowercase : Optional[int]=False ) -> Union[str, Any]:
"""simple docstrin... | 112 | 0 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
snake_case_ = logging.get_logger(__name__)
snake_case_ = {
'''google/bigbird-roberta-base''': '''https://huggingface.co/google/... | 214 |
from __future__ import annotations
from typing import Any
class SCREAMING_SNAKE_CASE__ :
def __init__( self , a , a , a = 0):
lowercase__ , lowercase__ : Dict = row, column
lowercase__ : Optional[Any] = [[default_va... | 214 | 1 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import RoFormerConfig, 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,... | 363 |
'''simple docstring'''
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
FEATURE_EXTRACTOR_MAPPING,
AutoConfig,
AutoFeatureExtractor,
WavaVecaConfig,
WavaVecaFeatureExtractor,
)
from transformers.... | 98 | 0 |
import os
def UpperCAmelCase_ ( __UpperCAmelCase : str = "input.txt" ) -> int:
with open(os.path.join(os.path.dirname(__UpperCAmelCase ) , __UpperCAmelCase ) ) as input_file:
SCREAMING_SNAKE_CASE_ = [
[int(__UpperCAmel... | 225 |
import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union
import numpy as np
def UpperCAmelCase_ ( __UpperCAmelCase : bytes , __UpperCAmelCase : int ) -> np.array:
SCREAMING_SNAKE_CASE_ = f"{sampling_rate}"
SCREAMING_SN... | 225 | 1 |
"""simple docstring"""
import json
import os
from typing import Dict, List, Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
A_ = logging.get_logger(__name__)
A_ = {
'''vocab_file''': '''vocab.json''',
'''... | 296 |
"""simple docstring"""
import warnings
from ..trainer import Trainer
from ..utils import logging
A_ = logging.get_logger(__name__)
class __SCREAMING_SNAKE_CASE ( UpperCamelCase ):
def __init__( self : Optional[int] , snake_case : List[str]=None , ... | 296 | 1 |
"""simple docstring"""
def _snake_case ( lowercase__ : list[int] ) -> list[list[int]]:
'''simple docstring'''
lowerCAmelCase_ :Optional[Any] = []
if len(lowercase__ ) == 1:
return [nums.copy()]
for _ in range(len(lowercase__ ) ):
... | 84 |
from __future__ import annotations
import unittest
from transformers import is_tf_available, is_torch_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, SMALL_MODEL_IDENTIFIER, is_pt_tf_cross_test, slow
if is_tf_available():
from transformers import (
... | 71 | 0 |
"""simple docstring"""
import numpy as np
class lowerCAmelCase :
'''simple docstring'''
def __init__( self ) -> Dict:
SCREAMING_SNAKE_CASE = (0, 0)
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE ... | 38 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__UpperCamelCase = {'''configuration_opt''': ['''OP... | 38 | 1 |
from __future__ import annotations
from math import gcd
def _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = 2 , SCREAMING_SNAKE_CASE = 1 , SCREAMING_SNAKE_CASE = 3 , ):
if num < 2:
raise ValueError('''The input value cannot be less than 2''' )
# Because of the relat... | 186 |
def A ( lowercase ) -> list:
'''simple docstring'''
UpperCamelCase = len(lowercase )
for i in range(1 , lowercase ):
UpperCamelCase = collection[i]
UpperCamelCase = 0
UpperCamelCase = i - 1
while low <= high:
UpperCamelCase = (low + hig... | 222 | 0 |
import argparse
from collections import defaultdict
def __UpperCamelCase ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> Dict:
"""simple docstring"""
A : List[str] = f'''{file}_{class_name}_{... | 367 |
from __future__ import annotations
def __UpperCamelCase ( _lowerCAmelCase ) -> list[int]:
"""simple docstring"""
A : Tuple = 2
A : List[Any] = []
while i * i <= n:
if n % i:
i += 1
else:
n //= i
... | 115 | 0 |
'''simple docstring'''
a__ : Union[str, Any] = [sum(int(c, 1_0) ** 2 for c in i.__str__()) for i in range(1_0_0_0_0_0)]
def _UpperCamelCase ( __A ) -> int:
'''simple docstring'''
UpperCamelCase__ = 0
while number:
# In... | 80 |
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiff... | 101 | 0 |
'''simple docstring'''
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F
from transformers import DetrImageProcessor, TableTransformerConfig, TableTransf... | 356 |
'''simple docstring'''
from __future__ import annotations
def __UpperCamelCase ( _UpperCAmelCase ):
if not nums:
raise ValueError("List is empty" )
return sum(_UpperCAmelCase ) / len(_UpperCAmelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 37 | 0 |
'''simple docstring'''
from __future__ import annotations
import math
import numpy as np
from numpy.linalg import norm
def lowerCAmelCase_ ( snake_case__ , snake_case__ ):
'''simple docstring'''
return math.sqrt(sum(pow(a - b , 2 ) for a,... | 3 |
'''simple docstring'''
import unittest
from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_ten... | 349 | 0 |
"""simple docstring"""
def _UpperCAmelCase ( __lowerCamelCase : int , __lowerCamelCase : int ) -> int:
return number | (1 << position)
def _UpperCAmelCase ( __lowerCamelCase : int , __lowerCamelCase : int ) -> int:
return number & ~(1 << position)
def ... | 371 |
"""simple docstring"""
import os
import sys
import unittest
UpperCAmelCase__ = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))
import check_dummies # noqa: E402
from check_dummies import create_dummy_files, ... | 40 | 0 |
"""simple docstring"""
import argparse
import shlex
import runhouse as rh
if __name__ == "__main__":
# Refer to https://runhouse-docs.readthedocs-hosted.com/en/latest/api/python/cluster.html#hardware-setup for cloud access
# setup instructions, if using on-demand hardware
# If user passes --user <us... | 54 |
'''simple docstring'''
import io
import json
import fsspec
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.json import JsonDatasetReader, JsonDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def a (... | 97 | 0 |
'''simple docstring'''
from manim import *
class _lowerCamelCase ( __snake_case ):
'''simple docstring'''
def __lowerCAmelCase ( self : Any ) -> Any:
__magic_name__ : int = Rectangle(height=0.5 , width=0.5 )
__magic_name__ : int ... | 352 |
'''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 ...schedulers import DD... | 275 | 0 |
def UpperCAmelCase__ ( _A : List[str] , _A : List[Any] , _A : Dict , _A : int=None ):
'''simple docstring'''
a__ =(path or []) + [u]
for v in graph[u]:
if visited_edge[u][v] is False:
a__ =True, True
a__ =dfs(_lowerCamelCase , ... | 188 |
'''simple docstring'''
import warnings
from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline # noqa F401
warnings.warn(
'''The `inpainting.py` script is outdated. Please use directly `from diffusers import'''
''' StableDiffusionInpaintPipeline` instead.'''
) | 112 | 0 |
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_inputs
if is_torch_available():
impo... | 362 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applic... | 304 | 0 |
'''simple docstring'''
from math import sqrt
def __snake_case ( UpperCAmelCase_ : int ):
assert isinstance(UpperCAmelCase_ , UpperCAmelCase_ ) and (
number >= 0
), "'number' must been an int and positive"
lowerCamelCase_ = True
# 0 and 1 are none p... | 55 | """simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ : int = logging.get_logger(__name__)
lowerCAmelCase__ : str = {
'facebook/xglm-564M': 'https://huggingface.co/facebook/xglm-564M/resolve/main/config.json',
# ... | 98 | 0 |
from dataclasses import dataclass
from typing import Optional
import numpy as np
import torch
import torch.nn as nn
from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_up_block
@... | 143 | # Lint as: python3
import itertools
import os
import re
UpperCamelCase__ = re.compile(R'([A-Z]+)([A-Z][a-z])')
UpperCamelCase__ = re.compile(R'([a-z\d])([A-Z])')
UpperCamelCase__ = re.compile(R'(?<!_)_(?!_)')
UpperCamelCase__ = re.compile(R'(_{2,})')
UpperCamelCas... | 143 | 1 |
from typing import List, Optional, Union
import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging
a_ ... | 330 |
from dataclasses import dataclass
from typing import Dict, Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .attention_processor import AttentionProcessor, AttnProcessor
from .modelin... | 330 | 1 |
from collections import OrderedDict
from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES
_UpperCAmelCase : Dict = logging.get_logger(__name__)
_UpperCAmelCase : Optional[int] ... | 362 |
import argparse
from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging
logging.set_verbosity_info()
def __lowerCamelCase ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase... | 200 | 0 |
import math
UpperCAmelCase_ : List[str] = 10
UpperCAmelCase_ : Optional[Any] = 7
UpperCAmelCase_ : Optional[Any] = BALLS_PER_COLOUR * NUM_COLOURS
def SCREAMING_SNAKE_CASE_ ( __magic_name__ : int = 20 ) -> str:
"""simple docstring"""... | 38 |
from collections.abc import Generator
from math import sin
def SCREAMING_SNAKE_CASE_ ( __magic_name__ : bytes ) -> bytes:
"""simple docstring"""
if len(__magic_name__ ) != 32:
raise ValueError("""Input must be of length 32""" )
UpperCamelCase :int =... | 38 | 1 |
"""simple docstring"""
import os
from datetime import datetime as dt
from github import Github
__A = [
'good first issue',
'good second issue',
'good difficult issue',
'enhancement',
'new pipeline/model',
'new scheduler',
'wip',
]
def _lowerCamelCase()... | 341 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__A = {
'configuration_swinv2': ['SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Swinv2Config'],
}
try:
if not is_torch_available():
raise Opti... | 341 | 1 |
'''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... | 58 |
"""simple docstring"""
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow
@require_torch
class lowerCamelCase__ ( unittest.TestCase ):
"""sim... | 115 | 0 |
"""simple docstring"""
from math import isclose, sqrt
def _lowerCAmelCase ( UpperCAmelCase__ : float, UpperCAmelCase__ : float, UpperCAmelCase__ : float ) ->tuple[float, float, float]:
A__ : List[Any] = point_y / 4 / point_x
A__ : ... | 363 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
A_ = {
'''configuration_llama''': ['''LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP... | 296 | 0 |
"""simple docstring"""
import gc
import math
import unittest
import torch
from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMixin,... | 263 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_lowerCAmelCase = logging.get_logger(__name__)
_lowerCAmelCase = {
'''facebook/xlm-... | 37 | 0 |
'''simple docstring'''
import os
from itertools import chain
from random import randrange, shuffle
import pytest
from .sola import PokerHand
a : int = (
'4S 3H 2C 7S 5H',
'9D 8H 2C 6S 7H',
'2D 6D 9D TH 7D',
'TC 8C 2S JH 6C',
'JH 8S TH AH QH',
'TS KS 5S 9S AC',
'KD 6S 9D... | 72 |
'''simple docstring'''
import os
import re
import sys
import traceback
import warnings
from pathlib import Path
from typing import Dict, Optional, Union
from uuid import uuida
from huggingface_hub import HfFolder, ModelCard, ModelCardData, hf_hub_download, whoami
from huggingface_hub.file_download import REGEX_C... | 72 | 1 |
"""simple docstring"""
import argparse
import json
import os
import torch
from transformers.file_utils import has_file
from diffusers import UNetaDConditionModel, UNetaDModel
snake_case__ : Optional[Any] = False
snake_case__ : Union[str, Any] = True
snake_case__ : Dict = False
... | 60 |
"""simple docstring"""
from itertools import permutations
def lowercase ( A_ )-> bool:
'''simple docstring'''
if num[3] % 2 != 0:
return False
if (num[2] + num[3] + num[4]) % 3 != 0:
return False
if num[5] % 5 != 0:
return False... | 40 | 0 |
"""simple docstring"""
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {
'google/pix2struct-textcaps-base': (
'https://h... | 366 |
"""simple docstring"""
import re
from filelock import FileLock
try:
import nltk
UpperCAmelCase__ = True
except (ImportError, ModuleNotFoundError):
UpperCAmelCase__ = False
if NLTK_AVAILABLE:
with FileLock('.lock') as lock:
nltk.download('punkt', quiet=True)
def _Upper... | 40 | 0 |
'''simple docstring'''
import unittest
from datasets import load_dataset
from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class _lowerCamelCase ( lowercase__ , uni... | 331 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
lowerCAmelCase :Union[str, Any] = {
'''configuration_vision_encoder_decoder''': ['''VisionEncode... | 331 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional
from packaging import version
if TYPE_CHECKING:
from ... import PreTrainedTokenizer, TensorType
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWith... | 370 |
from __future__ import annotations
from collections import deque
class _SCREAMING_SNAKE_CASE :
def __init__( self : Optional[Any] , __lowerCamelCase : list[str] ):
UpperCamelCase :list[dict] = []
self.adlist.append(
{"""value""": """""", """next_states""": [], ... | 62 | 0 |
"""simple docstring"""
import random
import unittest
from torch.utils.data import BatchSampler, DataLoader, IterableDataset
from accelerate import Accelerator
from accelerate.data_loader import (
BatchSamplerShard,
DataLoaderDispatcher,
DataLoaderShard,
IterableDataset... | 96 |
"""simple docstring"""
from typing import Dict
import numpy as np
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline, PipelineException
if is_tf_available():
import tensorflow as tf
from ..tf_utils impor... | 286 | 0 |
import os
import socket
from contextlib import contextmanager
import torch
from ..commands.config.default import write_basic_config # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
from .transformer_engine import c... | 84 |
from typing import List, Optional, Tuple, Union
import torch
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class _lowerCamelCase( _a ):
def __init__( self, lowerCamelCase, lowerCamelCase) -... | 84 | 1 |
import sys
def UpperCamelCase__ ( A__ ) -> Optional[int]:
snake_case__ : int = len(A__ )
snake_case__ : Union[str, Any] = [[0 for x in range(A__ )] for x in range(A__ )]
snake_case__ : int = [[0 for x in range(A__ ... | 143 | import random
from .binary_exp_mod import bin_exp_mod
def UpperCamelCase__ ( A__ , A__=1000 ) -> Optional[int]:
if n < 2:
return False
if n % 2 == 0:
return n == 2
# this means n is odd
snake_case__ : List[Any] = n - 1
snake_case... | 143 | 1 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class snake_case ( metaclass=_UpperCamelCase):
__UpperCamelCase = ['torch', 'scipy']
def __init__( self : str , *a__ : Tuple , **a__ : str ) -> Optional[Any]:
... | 163 |
"""simple docstring"""
from collections.abc import Generator
from math import sin
def a__ ( __lowercase ) -> bytes:
if len(__lowercase ) != 32:
raise ValueError("Input must be of length 32" )
_A = B""
for i in [3, 2, 1, 0]:
littl... | 163 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = {
'''microsoft/biogpt''': '''https://huggingface.co/microsoft/biogpt/resolve/main/config.json''',
# See all BioGPT models at https://hugg... | 8 |
'''simple docstring'''
import unittest
from knapsack import greedy_knapsack as kp
class lowercase__ ( unittest.TestCase ):
'''simple docstring'''
def UpperCAmelCase_ ( self ):
_SCREAMING_SNAKE_CASE : str = [10, 20, 30, 40, 50, 60]
... | 200 | 0 |
'''simple docstring'''
# using dfs for finding eulerian path traversal
def lowerCAmelCase_ ( snake_case__ , snake_case__ , snake_case__ , snake_case__=None ):
'''simple docstring'''
A : Optional[int] = (path or []) + ... | 359 |
'''simple docstring'''
from itertools import zip_longest
import requests
from bsa import BeautifulSoup
from pandas import DataFrame
def lowerCAmelCase_ ( snake_case__ = "laptop" ):
'''simple docstring'''
A : Tuple = F'https://www.amazon.i... | 311 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
_snake_case = {
'configuration_groupvit': [
'GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP',
'GroupViTCo... | 294 |
"""simple docstring"""
import unittest
import numpy as np
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ = None , ):
'''simple docstring'''
_a : List[Any] = np.shape(UpperCame... | 294 | 1 |
'''simple docstring'''
from statistics import mean
import numpy as np
def SCREAMING_SNAKE_CASE__ ( __A , __A , __A , __A ) -> list:
_snake_case = 0
# Number of processes finished
_snake_case = 0
# Displays the finished process.
... | 160 |
'''simple docstring'''
import torch
from transformers import AutoModel
class __UpperCAmelCase ( torch.nn.Module ):
def __init__( self , lowerCAmelCase_="sayef/fsner-bert-base-uncased" ):
"""simple docstring"""
super(lowerCAmelCase_ , self ).... | 160 | 1 |
def _lowercase ( lowercase__ , lowercase__ ):
__lowerCAmelCase : Dict = len(_SCREAMING_SNAKE_CASE )
print('''The following activities are selected:''' )
# The first activity is always selected
__lowerCAmelCase : Union[str, Any] = 0
... | 275 |
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.im... | 296 | 0 |
'''simple docstring'''
import inspect
import unittest
from transformers import ViTMSNConfig
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_configurat... | 217 |
'''simple docstring'''
def _lowerCAmelCase ( lowerCamelCase_ : str , lowerCamelCase_ : str ):
__lowercase = len(lowerCamelCase_ ) + 1
__lowercase = len(lowerCamelCase_ ) + 1
# dp is a 2d matrix where dp[i][j] denotes whether prefi... | 217 | 1 |
"""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/LICENS... | 72 |
"""simple docstring"""
def snake_case_ ( A_ : list[int], A_ : str ):
'''simple docstring'''
_lowerCamelCase : Tuple = int(A_ )
# Initialize Result
_lowerCamelCase : Dict = []
# Traverse through all deno... | 72 | 1 |
'''simple docstring'''
import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def a__ ( ) -> Optional[Any]:
"""simple docstring"""
with offli... | 357 |
'''simple docstring'''
def a__ ( _SCREAMING_SNAKE_CASE : list ) -> list:
"""simple docstring"""
UpperCAmelCase_ : str = len(_SCREAMING_SNAKE_CASE )
for _ in range(_SCREAMING_SNAKE_CASE ):
for i in range(_ % 2 , arr_size - 1 ... | 67 | 0 |
"""simple docstring"""
from collections import defaultdict
from math import ceil, sqrt
def _snake_case ( _snake_case : int = 1000000 , _snake_case : int = 10 ):
lowerCAmelCase : defaultdict = defaultdict(_snake_case )
for outer_width in range(3 , (t_limit //... | 60 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tensorflow_text_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__lowercase ... | 40 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A : Dict = {
"configuration_nllb_moe": [
"NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP",
"NllbMoeConfig",
]
}
try:
if not is_tor... | 259 |
"""simple docstring"""
def _lowerCamelCase ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ):
'''simple docstring'''
__lowerCAmelCase = [False] * len(_UpperCamelCase )
__lowerCAmelCase = []
queue.append(_UpperCamelCase )
__... | 259 | 1 |
'''simple docstring'''
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, DDIMS... | 63 |
import collections
import gzip
import os
import urllib
import numpy
from tensorflow.python.framework import dtypes, random_seed
from tensorflow.python.platform import gfile
from tensorflow.python.util.deprecation import deprecated
_A = collections.namedtuple('_Datasets', ['train', 'validation', 'test'... | 62 | 0 |
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging
lo... | 252 |
import os
import re
import warnings
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_ta import TaTokenize... | 252 | 1 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Callable
from typing import Any, Generic, TypeVar
__UpperCAmelCase = TypeVar('T')
class _SCREAMING_SNAKE_CASE ( Generic[T] ):
def __init__( self , __A ... | 84 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Generator
def _snake_case ( ) -> Generator[int, None, None]:
'''simple docstring'''
lowerCAmelCase_ :dict[int, int] = {}
lowerCAmelCase_ :int ... | 84 | 1 |
from .data_collator import (
DataCollatorForLanguageModeling,
DataCollatorForPermutationLanguageModeling,
DataCollatorForSeqaSeq,
DataCollatorForSOP,
DataCollatorForTokenClassification,
DataCollatorForWholeWordMask,
DataCollatorWithPadding,
DefaultDataCollator,
default_data_collator... | 257 |
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_tor... | 257 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
__A ={
'configuration_clip': ... | 163 |
'''simple docstring'''
def _UpperCamelCase ( UpperCamelCase__ ):
if isinstance(UpperCamelCase__ , UpperCamelCase__ ):
raise TypeError("""'float' object cannot be interpreted as an integer""" )
if isinstance(UpperCamelCase__ , UpperCamelCase__ ):
... | 163 | 1 |
'''simple docstring'''
from unittest import TestCase
from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset
class UpperCamelCase ( __SCREAMING_SNAKE_CASE ):
"""simple docstring"""
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any]):
... | 364 | '''simple docstring'''
import logging
from transformers.configuration_utils import PretrainedConfig
UpperCamelCase : Optional[Any] = logging.getLogger(__name__)
class UpperCamelCase ( a_ ):
"""simple docstring"""
A : Tuple = "masked_bert"
... | 345 | 0 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad
class lowercase ( unittest.TestCase ):
"""simple docstring"""
def lowerCAmelCase__ ( self ):
'''simple ... | 97 |
'''simple docstring'''
from jiwer import compute_measures
import datasets
a : List[Any] = "\\n@inproceedings{inproceedings,\n author = {Morris, Andrew and Maier, Viktoria and Green, Phil},\n year = {2004},\n month = {01},\n pages = {},\n title = {From WER and RIL to MER and WIL: improve... | 311 | 0 |
'''simple docstring'''
from typing import Dict, Iterable, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format, to_pil_image
from ...imag... | 360 |
'''simple docstring'''
def _lowerCAmelCase ( lowerCamelCase_ : str , lowerCamelCase_ : str ):
__lowercase = len(lowerCamelCase_ ) + 1
__lowercase = len(lowerCamelCase_ ) + 1
# dp is a 2d matrix where dp[i][j] denotes whether prefi... | 217 | 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... | 160 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A = {
'''configuration_git''': ['''GIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GitConfig''', '''GitVisionConfig'''],
'''processi... | 160 | 1 |
import tempfile
import unittest
from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
torch_device,
)
from ...generation.test_utils import GenerationTesterMixin
from ... | 330 |
import argparse
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration
UpperCamelCase__ : Any = [
# tf -> hf
("""/""", """."""),
("""layer_""", """layers... | 330 | 1 |
"""simple docstring"""
import os
import zipfile
import pytest
from datasets.utils.extract import (
BzipaExtractor,
Extractor,
GzipExtractor,
LzaExtractor,
SevenZipExtractor,
TarExtractor,
XzExtractor,
ZipExtractor,
ZstdExtractor,
)
from .utils import requ... | 217 |
"""simple docstring"""
from numpy import exp, pi, sqrt
def a__ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = 0.0 , __SCREAMING_SNAKE_CASE = 1.0 ) -> int:
return 1 / sqrt(2 * pi * sigma**2 ) * exp(-((x - mu) ** 2) / (2 * sigma**2) )
if __name__ == "__main__":
imp... | 217 | 1 |
"""simple docstring"""
from __future__ import annotations
# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
a_ = 10
def __lowercase ( snake_case_ : int ,snake_case_... | 369 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ....utils import _LazyModule
a_ = {"""tokenization_tapex""": ["""TapexTokenizer"""]}
if TYPE_CHECKING:
from .tokenization_tapex import TapexTokenizer
else:
import sys
a_ = _LazyModule(__name__, globals()["""... | 291 | 0 |
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=__UpperCAmelCase )
class _A ( __UpperCAmelCase ):
# `task` is not a ClassVar since we want it to be part of the `asdict` output ... | 49 | '''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .token... | 67 | 0 |
"""simple docstring"""
from __future__ import annotations
def _lowerCAmelCase ( UpperCamelCase_ ):
for i in range(1 , len(matrix[0] ) ):
matrix[0][i] += matrix[0][i - 1]
# preprocessing the first column
for i in range(1 , len(snake_case_ ) ):
matrix[i][0] +=... | 368 |
"""simple docstring"""
def _lowerCAmelCase ( UpperCamelCase_ , UpperCamelCase_ = False ):
if n == 2:
return True
if not n % 2 or n < 2:
return False
if n > 5 and n % 10 not in (1, 3, 7, 9): # can quickly check last digit
return False
if n > 3_3170_4406_4679_8873_8596_1981 a... | 255 | 0 |
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, DDIMScheduler, DDPMScheduler, StableDiffusionUpscalePipeline, UNetaDConditionModel
from diffusers.utils import... | 259 |
from math import factorial
__snake_case = {str(digit): factorial(digit) for digit in range(10)}
def _A ( SCREAMING_SNAKE_CASE__ : int ):
if not isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
raise TypeError('''Parameter number must be int''' ... | 259 | 1 |
"""simple docstring"""
from __future__ import annotations
import math
class A__ :
def __init__( self , _SCREAMING_SNAKE_CASE ):
__lowerCAmelCase : List[Any] = size
# approximate the overall size of segment tree with given value
__lowerCAmelCase : List[... | 182 |
"""simple docstring"""
import unittest
from transformers import AlbertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import Mo... | 182 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import XLMRobertaTokenizer
from diffusers import (
AltDiffusionImgaImgPipeline,
AutoencoderKL,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.pipe... | 252 |
import argparse
import gc
import json
import os
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 Accelerat... | 252 | 1 |
"""simple docstring"""
__A = "\n# Installazione di Transformers\n! pip install transformers datasets\n# Per installare dalla fonte invece dell'ultima versione rilasciata, commenta il comando sopra e\n# rimuovi la modalità commento al comando seguente.\n# ! pip install git+https://github.com/huggingf... | 2 | """simple docstring"""
import os
import re
import shutil
from argparse import ArgumentParser, Namespace
from datasets.commands import BaseDatasetsCLICommand
from datasets.utils.logging import get_logger
__A = "<<<<<<< This should probably be modified because it mentions: "
__A = "=... | 2 | 1 |
def __lowercase ( a__ , a__ ) -> int:
return int((input_a, input_a).count(0 ) != 0 )
def __lowercase ( ) -> None:
assert nand_gate(0 , 0 ) == 1
assert nand_gate(0 , 1 ) == 1
assert nand_gate(1 , 0 ) == 1
assert nand_gat... | 257 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece... | 257 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase = {
'configuration_blenderbot': [
... | 366 |
'''simple docstring'''
def _snake_case ( _SCREAMING_SNAKE_CASE : list ) -> list:
"""simple docstring"""
if len(_SCREAMING_SNAKE_CASE ) < 2:
return collection
def circle_sort_util(_SCREAMING_SNAKE_CASE : list , _SCREAMING_SNAKE_CASE ... | 187 | 0 |
'''simple docstring'''
import json
import os
import shutil
import warnings
from argparse import ArgumentParser, Namespace
from pathlib import Path
from typing import List
from ..utils import logging
from . import BaseTransformersCLICommand
try:
from cookiecutter.main import cookiecutter
lowercase... | 58 |
import json
import os
import unittest
from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class _snake_case ( __snake_case , unittest.TestCase ):
'''simple docstring'''
A... | 345 | 0 |
from __future__ import annotations
lowerCamelCase_ : Union[str, Any] = 1.6021E-19 # units = C
def A__ ( lowerCamelCase , lowerCamelCase , lowerCamelCase , ) -> tuple[str, float]:
if (conductivity, electron_conc, mobility).count(0 ) != 1:
... | 351 |
def A__ ( lowerCamelCase ) -> list:
return [
txt[:a] + txt[a].upper() + txt[a + 1 :]
for a in range(len(lowerCamelCase ) )
if txt[a].isalpha()
]
if __name__ == "__main__":
__import__("""doctest""").testmod()
| 223 | 0 |
'''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,
... | 93 |
"""simple docstring"""
from numpy import exp, pi, sqrt
def a__ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = 0.0 , __SCREAMING_SNAKE_CASE = 1.0 ) -> int:
return 1 / sqrt(2 * pi * sigma**2 ) * exp(-((x - mu) ** 2) / (2 * sigma**2) )
if __name__ == "__main__":
imp... | 217 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ : List[Any] = logging.get_logger(__name__)
A_ : str = {
'microsoft/markuplm-base': 'https://huggingface.co/microsoft/markuplm-base/resolve/main/config.json',
'microsoft/markuplm-large... | 370 |
import os
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_doctest_list.py
A_ : List[str] = '.'
if __name__ == "__main__":
A_ : Dict = os.path.join(REPO_PATH, 'utils/documentation_tests.txt')
... | 292 | 0 |
import tempfile
import unittest
from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
torch_device,
)
from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_common... | 330 |
import gc
import unittest
from parameterized import parameterized
from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow
if is_flax_available():
import jax
import jax.numpy as jnp
@slow
@requ... | 330 | 1 |
import warnings
from functools import wraps
from typing import Callable
def __a ( lowerCAmelCase_ : Callable ) -> Callable:
'''simple docstring'''
@wraps(lowerCAmelCase_ )
def _inner_fn(*lowerCAmelCase_ : List[Any] ,**lowerCAmelCase_ : Tuple ... | 277 |
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 ...te... | 277 | 1 |
def A_ ( _lowerCAmelCase ) -> str:
UpperCamelCase : Tuple = 0
# if input_string is "aba" than new_input_string become "a|b|a"
UpperCamelCase : Any = ""
UpperCamelCase : List[Any] = ""
# append each character + "|" in new_string for range(0, length-1)... | 52 |
"""simple docstring"""
from typing import List, Optional, Union
import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, T... | 266 | 0 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTest... | 341 |
"""simple docstring"""
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 SPIECE_UNDERLINE, logging
__A = logging.get_l... | 341 | 1 |
import warnings
from typing import Dict
import numpy as np
from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQUENCE_CLASSIFICATI... | 118 |
"""simple docstring"""
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments
import transformers
from transformers import (
AutoConfi... | 255 | 0 |
"""simple docstring"""
def __lowercase ( _a ):
snake_case_ : str = [int(UpperCAmelCase_ ) for i in ip_va_address.split('''.''' ) if i.isdigit()]
return len(UpperCAmelCase_ ) == 4 and all(0 <= int(UpperCAmelCase_ ) <= 254 for octet in octets )
if __name__ == "__main__":... | 357 |
"""simple docstring"""
import math
import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def __lowercase ( _a = 3 ):
if isinstance(_a , _a ):
raise TypeError('''number of qubits must be a integer.''' )
if num... | 155 | 0 |
def A ( _lowercase = 10**9 ):
SCREAMING_SNAKE_CASE : Dict = 1
SCREAMING_SNAKE_CASE : str = 2
SCREAMING_SNAKE_CASE : str = 0
SCREAMING_SNAKE_CASE : str = 0
SCREAMING_SNAKE_CASE : ... | 182 | import math
def A ( _lowercase ):
return math.sqrt(_lowercase ) * math.sqrt(_lowercase ) == num
def A ( _lowercase ):
SCREAMING_SNAKE_CASE : Dict = 0
SCREAMING_SNAKE_CASE : Tuple = n
while left <= right:
... | 182 | 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 is_vision_available(... | 371 |
"""simple docstring"""
def lowerCamelCase (a_ :list , a_ :list , a_ :int , a_ :int , a_ :int) -> int:
if index == number_of_items:
return 0
lowercase :Optional[int] = 0
lowercase :str = 0
lowercase ... | 172 | 0 |
'''simple docstring'''
lowerCamelCase : Union[str, Any] = '\n# Installazione di Transformers\n! pip install transformers datasets\n# Per installare dalla fonte invece dell\'ultima versione rilasciata, commenta il comando sopra e\n# rimuovi la modalità commento al comando seguente.\n# ! pip install gi... | 2 |
'''simple docstring'''
import unittest
from transformers import TrOCRConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import Mo... | 2 | 1 |
import os
from datetime import datetime as dt
from github import Github
__UpperCAmelCase : int = [
"good first issue",
"feature request",
"wip",
]
def A__ ( ) -> Any:
__snake_case: List[Any] = Github(os.environ["""GITHUB_TOKEN"""])
__sn... | 371 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
... | 293 | 0 |
'''simple docstring'''
import re
def a_ ( lowerCamelCase : Union[str, Any] ):
if len(re.findall('[ATCG]' , _A ) ) != len(_A ):
raise ValueError('Invalid Strand' )
return dna.translate(dna.maketrans('ATCG' , 'TAGC' ) )
if __name__ ==... | 4 |
from __future__ import annotations
lowercase__ : str = [
[-1, 0], # left
[0, -1], # down
[1, 0], # right
[0, 1], # up
]
def lowerCamelCase__ ( _A , _A , _A , _A , _A , ):
'''simple docstring'''
snake_case_... | 187 | 0 |
from __future__ import annotations
def __A ( __lowerCamelCase , __lowerCamelCase ) -> int:
print(f'Vertex\tShortest Distance from vertex {src}' )
for i, d in enumerate(__lowerCamelCase ):
print(f'{i}\t\t{d}' )
def __A ( __lowerCamelCase , ... | 347 |
__UpperCamelCase : Dict = "ABCDEFGHIJKLMNOPQRSTUVWXYZ"
def __A ( ) -> None:
a = input("""Enter message: """ )
a = input("""Enter key [alphanumeric]: """ )
a = input("""Encrypt/Decrypt [e/d]: """ )
if mode.lower().startswit... | 347 | 1 |
"""simple docstring"""
import operator as op
def lowercase ( A_ )-> Any:
'''simple docstring'''
a : Tuple = []
a : int = lambda A_ , A_ : int(x / y ) # noqa: E731 integer division operation
a... | 40 |
'''simple docstring'''
import collections
import gzip
import os
import urllib
import numpy
from tensorflow.python.framework import dtypes, random_seed
from tensorflow.python.platform import gfile
from tensorflow.python.util.deprecation import deprecated
lowerCAmelCase : Dict ... | 223 | 0 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionSAGPipeline,
UNetaDConditionModel,
)
from diffusers.utils import slow, torch... | 360 | '''simple docstring'''
from importlib import import_module
from .logging import get_logger
__a = get_logger(__name__)
class A__ :
"""simple docstring"""
def __init__( self : List[str] , lowerCAmelCase__ : List[Any] , lowerCAmelCase__ : O... | 17 | 0 |
class UpperCAmelCase_ :
'''simple docstring'''
def __init__( self : List[str] , UpperCamelCase__ : Union[str, Any] , UpperCamelCase__ : Tuple ) -> List[Any]:
"""simple docstring"""
__magic_name__ = name
... | 88 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
_snake_case : Union[str, Any] = {
'configuration_encodec': [
'ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP',
'EncodecConfig... | 292 | 0 |
import warnings
from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor
_lowerCamelCase : Optional[Any] = logging.get_logger(__name__)
class __snake_case (_a ):
def __init__( self : str , *_UpperCAmelCase : Any , **_UpperCAmel... | 369 |
from collections import namedtuple
import requests
from lxml import html # type: ignore
_lowerCamelCase : Dict = namedtuple("covid_data", "cases deaths recovered")
def _UpperCAmelCase (UpperCamelCase_ : str = "https://www.worldometers.info/coronavirus/" ):
'''simple docstring'''
... | 159 | 0 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
a_ :Any = logging.get_logger(__name__)
a_ :Optional[Any] = ... | 277 |
import itertools
import random
import unittest
import numpy as np
from transformers import BatchFeature, SpeechTaFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import SequenceFeat... | 277 | 1 |
"""simple docstring"""
import inspect
import unittest
import warnings
from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)... | 320 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
UpperCAmelCase : Dict = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except Opt... | 320 | 1 |
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