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'''
from __future__ import annotations
from collections.abc import Iterator
from typing import Any
class SCREAMING_SNAKE_CASE :
def __init__( self : str , lowercase__ : Any ):
'''simple docstring'''
... | 442 |
'''simple docstring'''
import os
import posixpath
import uuid
from dataclasses import dataclass
from typing import TYPE_CHECKING, Iterable, List, Optional, Tuple, Union
import numpy as np
import pyarrow as pa
import datasets
from datasets.arrow_writer import ArrowWriter, ParquetWriter
from d... | 442 | 1 |
"""simple docstring"""
import inspect
import unittest
from huggingface_hub import hf_hub_download
from transformers import ASTConfig
from transformers.testing_utils import require_torch, require_torchaudio, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_torchaudio_... | 422 |
"""simple docstring"""
import pytest
from datasets.parallel import ParallelBackendConfig, parallel_backend
from datasets.utils.py_utils import map_nested
from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows
def snake_case__ ( _SCREAMING_SNAKE_CASE ) ->Dict: #... | 422 | 1 |
import warnings
from ...utils import logging
from .image_processing_videomae import VideoMAEImageProcessor
lowercase_ = logging.get_logger(__name__)
class __lowerCAmelCase ( __UpperCAmelCase ):
def __init__( self , *lowerCAmelCase , **lowerCAmelCase ... | 291 |
import argparse
import csv
import logging
import os
import random
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange
from transformers import (
CONFIG_NAME,
WEIGHTS_NAME,
AdamW,
OpenAIGPTDoubleHead... | 431 | 0 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_deformable_detr import DeformableDetrImageProcessor
a : Tuple = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE__ ( _UpperCamelCase ):
def __init__( self : ... | 680 |
'''simple docstring'''
import tempfile
import unittest
from make_student import create_student_by_copying_alternating_layers
from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch
a : Dict = '''... | 680 | 1 |
import unittest
from transformers import is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is... | 401 |
"""simple docstring"""
import argparse
import json
import os
import pickle
import shutil
import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset
from transformers import (
BertConfig,
BertForMaskedLM,
BertTokenizer,
DistilBe... | 49 | 0 |
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Acce... | 214 |
from __future__ import annotations
import unittest
from transformers import LEDConfig, 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
from ...test_pipeline... | 214 | 1 |
"""simple docstring"""
import torch
from diffusers import UnCLIPScheduler
from .test_schedulers import SchedulerCommonTest
class __A ( A_ ):
'''simple docstring'''
lowerCAmelCase : int = (UnCLIPScheduler,)
de... | 560 |
"""simple docstring"""
from collections import OrderedDict
from typing import List, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase_ = logging.get_logger(__name__... | 560 | 1 |
import argparse
from collections import defaultdict
import yaml
_UpperCamelCase : str = """docs/source/en/_toctree.yml"""
def __UpperCamelCase ( snake_case ) -> Any:
'''simple docstring'''
__A = defaultdict(snake_case )
__A = []
... | 341 |
import argparse
import json
from collections import OrderedDict
import torch
from huggingface_hub import cached_download, hf_hub_url
from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification
def __UpperCamelCase ( snake_case ) -> Dict:
'''simple docstring'''
... | 341 | 1 |
"""simple docstring"""
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.models import Sequential
if __name__ == "__main__":
a_ = pd.read_csv('sample_data.csv', header=Non... | 76 | """simple docstring"""
from math import sqrt
def lowerCamelCase_ ( __lowerCAmelCase ) -> bool:
'''simple docstring'''
assert isinstance(__lowerCAmelCase , __lowerCAmelCase ) and (
number >= 0
), "'number' must been an int and positive"
... | 530 | 0 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__lowercase = logging.get_logger(__name__)
__lowercase = {
"distilbert-base-uncased": "https://hug... | 707 | """simple docstring"""
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _lowercase ( __a ):
"""simple docstring"""
lowercase__ = ['''image_processor''', '''tokenizer''']
l... | 296 | 0 |
"""simple docstring"""
a :Tuple = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"
def _lowercase ( __lowerCAmelCase ) -> List[str]:
# Make sure the supplied data is a bytes-like object
if not isinstance(snake_case__ , snake_case__ ):
... | 680 |
import argparse
import requests
import torch
from PIL import Image
from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor
def a ( snake_case__: List[Any] ):
'''simple docstring'''
if "cls_token" in name:
lowercase_ = name.replace(... | 97 | 0 |
"""simple docstring"""
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=_UpperCAmelCase )
class _UpperCAmelCase( _UpperCAmelCase ):
lowercase__ = fi... | 704 |
"""simple docstring"""
import argparse
import torch
from transformers import OpenAIGPTConfig, OpenAIGPTModel, load_tf_weights_in_openai_gpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def lowerCamelCase__ ( __snake_case, __snake_c... | 78 | 0 |
import unittest
from transformers import BertGenerationConfig, 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 ModelTester... | 89 |
'''simple docstring'''
from queue import PriorityQueue
from typing import Any
import numpy as np
def lowercase_ ( __A : dict , __A : str , __A : set , __A : set , __A : dict , __A : dict , __A : ... | 94 | 0 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils impo... | 717 |
from abc import ABC, abstractmethod
from typing import Optional, Union
from .. import Dataset, DatasetDict, Features, IterableDataset, IterableDatasetDict, NamedSplit
from ..utils.typing import NestedDataStructureLike, PathLike
class _SCREAMING_SNAKE_CASE (UpperCamelCase ):
def __init__( sel... | 447 | 0 |
"""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,
)
if is_sentencepiece_available():
fro... | 532 |
"""simple docstring"""
class lowercase :
def __init__( self ) -> Any:
lowerCAmelCase = """"""
lowerCAmelCase = """"""
lowerCAmelCase = []
def _snake_case ( self , lowercase , lowercase ) ... | 532 | 1 |
"""simple docstring"""
import enum
import warnings
from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
from ..models.auto.modeling_t... | 719 |
"""simple docstring"""
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class a_ ( _UpperC... | 19 | 0 |
from __future__ import annotations
import unittest
import numpy as np
from transformers import LayoutLMConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, r... | 80 |
"""simple docstring"""
from ..utils import is_flax_available, is_torch_available
if is_torch_available():
from .autoencoder_kl import AutoencoderKL
from .controlnet import ControlNetModel
from .dual_transformer_ad import DualTransformeraDModel
from .modeling_utils import ModelMixin
from .prior_transfo... | 129 | 0 |
from collections import deque
def __lowercase ( _SCREAMING_SNAKE_CASE ) -> List[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = len(__lowerCAmelCase )
SCREAMING_SNAKE_CASE = deque()
SCREAMING_SNAKE_CASE = [False for _ in range(__lowerCA... | 709 |
import warnings
from ...utils import logging
from .image_processing_deformable_detr import DeformableDetrImageProcessor
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
class UpperCamelCase__ ( lowerCAmelCase_ ):
'''simple docstring'''
def __init__( ... | 116 | 0 |
'''simple docstring'''
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image
from tran... | 18 |
'''simple docstring'''
import unittest
from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_com... | 245 | 0 |
"""simple docstring"""
import argparse
import shutil
from pathlib import Path
from tqdm import tqdm
from transformers import AutoTokenizer
def lowercase (SCREAMING_SNAKE_CASE_ : Tuple , SCREAMING_SNAKE_CASE_ : Optional[int] , SCREAMING_SNAKE_CASE_ : List... | 327 |
"""simple docstring"""
from collections import defaultdict
from math import gcd
def lowercase (SCREAMING_SNAKE_CASE_ : int = 1_50_00_00 ) -> int:
SCREAMING_SNAKE_CASE = defaultdict(SCREAMING_SNAKE_CASE_ )
SCREAMING_SNAKE_CASE = 2
... | 327 | 1 |
from collections import defaultdict
from math import gcd
def _A ( __snake_case :int = 150_0000 ) -> int:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = defaultdict(__snake_case )
__SCREAMING_SNAKE_CASE = 2
while 2 * euclid_m * (euclid_m + 1... | 693 |
import unittest
from transformers import is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
i... | 693 | 1 |
'''simple docstring'''
import argparse
import os
import re
_lowercase : List[Any] = "src/transformers"
# Pattern that looks at the indentation in a line.
_lowercase : str = re.compile(r"^(\s*)\S")
# Pattern that matches `"key":" and puts `key` in group 0.
_lowercase : str = re... | 30 | '''simple docstring'''
import unittest
import numpy as np
def lowerCamelCase ( UpperCAmelCase__ : np.ndarray , UpperCAmelCase__ : np.ndarray , UpperCAmelCase__ : np.ndarray , UpperCAmelCase__ : np.ndarray | None = None , ) -> np.ndarray:
lowercase_ ... | 30 | 1 |
import math
__lowerCamelCase = 10
__lowerCamelCase = 7
__lowerCamelCase = BALLS_PER_COLOUR * NUM_COLOURS
def UpperCamelCase ( __lowerCamelCase : List[Any] = 20 ):
snake_case : Any = math.comb(__a , __a )
snake_case : Option... | 204 |
"""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 snake_case ( __lowercase , ... | 626 | 0 |
'''simple docstring'''
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... | 709 |
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorFormatt... | 410 | 0 |
import numpy as np
import qiskit
def snake_case( __magic_name__ = 8 , __magic_name__ = None ) -> str:
'''simple docstring'''
lowercase : Union[str, Any] = np.random.default_rng(seed=__magic_name__ )
# Roughly 25... | 217 |
from __future__ import annotations
def snake_case( __magic_name__ , __magic_name__ ) -> list[list[int]]:
'''simple docstring'''
lowercase : list[list[int]] = []
lowercase : list[int] = []
lowercase ... | 217 | 1 |
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 .tokenization_barthez... | 702 | from itertools import product
def lowerCamelCase ( UpperCamelCase : int , UpperCamelCase : int ) -> list[int]:
_lowerCamelCase = sides_number
_lowerCamelCase = max_face_number * dice_number
_lowerCamelCase = [0] * ... | 234 | 0 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
SCREAMING_SNAKE_CASE = logging.get_logger(__name... | 99 |
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 accelerat... | 154 | 0 |
"""simple docstring"""
import math
def lowerCamelCase_ ( _lowerCamelCase : int ):
lowerCamelCase_ = []
lowerCamelCase_ = 2
lowerCamelCase_ = int(math.sqrt(_lowerCamelCase ) ) # Size of every segment
lowerCamelCase_ ... | 66 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
__lowercase : List[str] = logging.get_logger(__name__)
class lowerCAmelCase ( a ):
"""simple docstring"""
def __init__( self , *UpperCamel... | 66 | 1 |
'''simple docstring'''
from math import ceil, sqrt
def snake_case_ ( SCREAMING_SNAKE_CASE__ = 1_00_00_00 ):
'''simple docstring'''
_snake_case = 0
for outer_width in range(3 , (limit // 4) + 2 ):
if outer_width**2 > limit:
_snake_case = ... | 672 |
'''simple docstring'''
import math
def snake_case_ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
return math.pow(SCREAMING_SNAKE_CASE__ , 2 ) - a
def snake_case_ ( SCREAMING_SNAKE_CASE__ ):
'''s... | 672 | 1 |
import re
import string
from collections import Counter
import sacrebleu
import sacremoses
from packaging import version
import datasets
_lowerCAmelCase = """
@inproceedings{xu-etal-2016-optimizing,
title = {Optimizing Statistical Machine Translation for Text Simplification},
authors={Xu, ... | 716 |
def lowercase ( _a ) -> bool:
if not isinstance(_a ,_a ):
UpperCAmelCase_: Dict = f"Input value of [number={number}] must be an integer"
raise TypeError(_a )
if number < 0:
return False
UpperCAmelCase_: Dict = number * number
while number > 0... | 306 | 0 |
"""simple docstring"""
import argparse
import json
import torch
from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel
def lowerCamelCase_ (UpperCamelCase__ : Optional[int] , UpperCamelCase__ : Dict=1 ):
if n_shave_prefix_segments >= 0:
return ".".jo... | 506 |
"""simple docstring"""
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig
_lowerCAmelCase :Tuple = logging.get_logger(__name__)
_lowerCAmelCase :Union[str, Any] = {
'Intel/dpt-large': 'https://huggingface.co/Intel/dpt-larg... | 506 | 1 |
from numpy import exp, pi, sqrt
def SCREAMING_SNAKE_CASE ( snake_case__ , snake_case__ = 0.0 , snake_case__ = 1.0 ) -> int:
return 1 / sqrt(2 * pi * sigma**2 ) * exp(-((x - mu) ** 2) / (2 * sigma**2) )
if __name__ == "__main__":
import doctest
docte... | 142 |
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 SCREAMING_SNAKE_CASE ( ... | 142 | 1 |
import argparse
import json
import os
import torch
from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def UpperCAmelCase_ ( _UpperCAmelCase :Union[str, Any] , _UpperCAmelCase :... | 188 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE__ ( _lowercase : list[float] , _lowercase : Tuple ) -> int:
'''simple docstring'''
print(f"""Vertex\tShortest Distance from vertex {src}""" )
for i, d in enumerate(_lowercase ):
pri... | 266 | 0 |
from ..utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_scipy_available,
is_torch_available,
is_torchsde_available,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ..utils.dum... | 52 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import YolosConfig, YolosForObjectDetection, YolosImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
SCREAMING_SNA... | 52 | 1 |
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by ap... | 68 |
import random
def __magic_name__ ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = False ) -> dict:
_lowercase : dict = {i: [] for i in range(SCREAMING_SNAKE_CASE )}
# if probability is greater or equal than 1, then gen... | 66 | 0 |
from __future__ import annotations
from collections.abc import Iterator
from typing import Generic, TypeVar
_UpperCAmelCase : Tuple = TypeVar("T")
class lowercase ( Generic[T] ):
def __init__( self , A_ ) -> Any:
"""simple docstring"""
UpperCamelCa... | 709 |
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_memor... | 3 | 0 |
import torch
def A__( ):
if torch.cuda.is_available():
_snake_case : int = torch.cuda.device_count()
else:
_snake_case : Optional[int] = 0
print(F'''Successfully ran on {num_gpus} GPUs''' )
if __name__ == "__main__":
main()
... | 304 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase_ : List[str] = {
'''configuration_informer''': [
'''INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
... | 304 | 1 |
"""simple docstring"""
import re
import time
from typing import Optional
import IPython.display as disp
from ..trainer_callback import TrainerCallback
from ..trainer_utils import IntervalStrategy, has_length
def UpperCamelCase ( SCREAMING_SNAKE_CASE_ ) ->Dict:
_lowerCamelCase : A... | 558 | """simple docstring"""
import json
import os
import unittest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers
from ...test_tokenization_common import Token... | 558 | 1 |
import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
UpperCamelCase__ = logging.get_logger(__name__)
class UpperCAmelCase__ ( A_ ):
'''simple docstring'''
def __init__( self : int , *UpperCamelCase : ... | 322 |
import os
UpperCamelCase__ = {'I': 1, 'V': 5, 'X': 10, 'L': 50, 'C': 100, 'D': 500, 'M': 1_000}
def UpperCamelCase__ ( UpperCAmelCase_ ) -> int:
'''simple docstring'''
_lowercase : Optional[int] = 0
_lowercase : Dic... | 322 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ : int = logging.get_logger(__name__)
a_ : str = {
'alibaba-damo/mgp-str-base': 'https://huggingface.co/alibaba-damo/mgp-str-base/resolve/main/config.json',
}
class _lowerCamelCase ( UpperCamel... | 704 | import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils i... | 107 | 0 |
import math
def __lowerCAmelCase( _SCREAMING_SNAKE_CASE ) -> str:
"""simple docstring"""
_A = 0
_A = 0
while num > 0:
_A = num % 8
_A = octal + (remainder * math.floor(math.pow(10 ... | 27 |
import sys
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be c... | 27 | 1 |
import re
from filelock import FileLock
try:
import nltk
lowercase_ = True
except (ImportError, ModuleNotFoundError):
lowercase_ = False
if NLTK_AVAILABLE:
with FileLock('''.lock''') as lock:
nltk.download('''punkt''', quiet=True)
def lowerCAmelCase ( Uppe... | 708 |
import argparse
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerat... | 336 | 0 |
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 transformers.generation import (
... | 570 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase_ : Optional[Any] = logging.get_logger(__name__)
UpperCAmelCase_ : ... | 570 | 1 |
def _UpperCAmelCase (UpperCamelCase_ : list[int] ):
'''simple docstring'''
_lowerCAmelCase : Any = []
if len(UpperCamelCase_ ) == 1:
return [nums.copy()]
for _ in range(len(UpperCamelCase_ ) ):
_lowerCAmelCase : Any ... | 196 |
import comet # From: unbabel-comet
import torch
import datasets
_lowerCamelCase : List[Any] = datasets.logging.get_logger(__name__)
_lowerCamelCase : Optional[Any] = "\\n@inproceedings{rei-EtAl:2020:WMT,\n author = {Rei, Ricardo and Stewart, Craig and Farin... | 196 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_UpperCamelCase = {
'configuration_xlm_roberta_xl': [
'XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP',
'XLMRoberta... | 179 |
"""simple docstring"""
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
BERT_... | 179 | 1 |
def a__ ( a ) -> List[Any]:
A_ : List[Any] = 1
A_ : Tuple = 2
while i * i <= n:
A_ : Tuple = 0
while n % i == 0:
n //= i
multiplicity += 1
... | 236 | 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,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimens... | 236 | 1 |
"""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... | 52 |
'''simple docstring'''
from __future__ import annotations
def __a ( A__ ) -> int:
if not nums:
return 0
lowerCAmelCase = nums[0]
lowerCAmelCase = 0
for num in nums[1:]:
lowerCAmelCase , lowerCAmelCase = (
max_excludi... | 649 | 0 |
import unittest
import numpy as np
from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available... | 608 |
from __future__ import annotations
from collections.abc import Iterator
class lowerCamelCase :
"""simple docstring"""
def __init__( self , __UpperCamelCase ):
A_ = value
A_ = None
A_ = None
class lo... | 608 | 1 |
from typing import List, Union
import numpy as np
from ..utils import add_end_docstrings, 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 ..image_utils import loa... | 360 |
SCREAMING_SNAKE_CASE: Optional[int] = {str(digit): digit**5 for digit in range(1_0)}
def _a ( lowerCAmelCase )-> int:
return sum(DIGITS_FIFTH_POWER[digit] for digit in str(lowerCAmelCase ) )
def _a ( )-> int:
return sum(
number
for number... | 360 | 1 |
from .imports import is_tqdm_available
if is_tqdm_available():
from tqdm.auto import tqdm as _tqdm
from ..state import PartialState
def __magic_name__ ( __a : bool = True , *__a : Any , **__a : List[Any] ):
'''simple docstring'''
if not is_tqdm_available():
ra... | 718 |
from ..utils import DummyObject, requires_backends
class __A( metaclass=__lowerCamelCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = ["""torch""", """torchsde"""]
def __init__(self , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ):
requires_backends(self... | 86 | 0 |
import argparse
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import (
RobertaTokenizer,
TrOCRConfig,
TrOCRForCausalLM,
TrOCRProcessor,
VisionEncoderDecoderModel,
ViTConfig,
ViTImageProcessor,
ViTModel,
)
from transformers.utils import l... | 612 |
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,
to_channel_dimension_format,
)
from ... | 612 | 1 |
import argparse
import copy
def _a ( lowerCamelCase__ ) -> Tuple:
lowerCamelCase_ : Optional[Any] = {}
with open(lowerCamelCase__ ) as f:
for line in f:
if line.split()[0] not in dict_of_neighbours:
lowerCamelCase_ : Dict = []
... | 144 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase = logging.get_logger(__name__)
UpperCamelCase = {
'''EleutherAI/gpt-neox-20b''': '''https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json''',
# See all GPTNeoX models at https:... | 144 | 1 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_dpt import DPTImageProcessor
a : Dict = logging.get_logger(__name__)
class __UpperCAmelCase( snake_case__ ):
"""simple docstring"""
... | 218 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A : List[Any] = {
'configuration_lilt': ['LILT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LiltConfig'],
}
try:
if not is_torch_available():
... | 516 | 0 |
"""simple docstring"""
import argparse
from pathlib import Path
from typing import Dict, OrderedDict, Tuple
import torch
from audiocraft.models import MusicGen
from transformers import (
AutoFeatureExtractor,
AutoTokenizer,
EncodecModel,
MusicgenDecoderConfig,
MusicgenForConditi... | 556 |
"""simple docstring"""
import os
from argparse import ArgumentParser
from typing import List
import torch.utils.data
from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node
SCREAMING_SNAKE_CASE = 4
SCREAMING_SNAKE_CASE = 3
class... | 556 | 1 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_convbert import ConvBertTokenizer
a_ = logging.get_l... | 76 |
'''simple docstring'''
# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from diffusers.confi... | 131 | 0 |
import argparse
import json
import torch
from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel
def _lowerCAmelCase ( A__ , A__=1 ):
if n_shave_prefix_segments >= 0:
return ".".join(path.split('.' )[n_shave_prefix_segments:] )
else:
return ".".jo... | 642 |
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XGLMTokenizer, XGLMTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_tokenization_com... | 642 | 1 |
import argparse
import re
import torch
from CLAP import create_model
from transformers import AutoFeatureExtractor, ClapConfig, ClapModel
a__ = {
'''text_branch''': '''text_model''',
'''audio_branch''': '''audio_model.audio_encoder''',
'''attn''': '''attention.self''',
... | 14 |
import os
import pytest
from datasets import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
)
__lowercase : Optional[Any] = pytest.mark.integration
@pytest.mark.parametrize("""path""" ,... | 36 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__ = logging.get_logger(__name__)
UpperCamelCase__ = {
'''caidas/swin2sr-classicalsr-x2-64''': (
'''https://huggingface.co/caidas/swin2sr-classicalsr-x2-64/resolve/main... | 143 |
import qiskit
def UpperCAmelCase__ ( _A , _A ):
"""simple docstring"""
a_ = qiskit.Aer.get_backend('''aer_simulator''' )
a_ = qiskit.QuantumCircuit(4 , 2 )
# encode inputs in qubits 0 and 1
if bita == 1:
qc_ha.x(... | 143 | 1 |
import json
import logging
import os
import socket
import git
import numpy as np
import torch
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d - %(message)s''',
datefmt='''%m/%d/%Y %H:%M:%S''',
level=logging.INFO,
)
snake_case__ = logging.getLogger(_... | 395 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__a : Optional[int] = {"configuration_xlnet": ["XLNET_PRETRAINED_CONFIG_ARCHIVE_... | 637 | 0 |
import coval # From: git+https://github.com/ns-moosavi/coval.git # noqa: F401
from coval.conll import reader, util
from coval.eval import evaluator
import datasets
_lowerCamelCase = datasets.logging.get_logger(__name__)
_lowerCamelCase = '''\
@InProceedings{moosavi2019minimum,
author ... | 721 |
import argparse
import logging
import os
import time
import timeit
import datasets
import numpy as np
import pycuda.autoinit # noqa: F401
import pycuda.driver as cuda
import tensorrt as trt
import torch
from absl import logging as absl_logging
from accelerate import Accelerator
from datasets import load_dataset, ... | 447 | 0 |
'''simple docstring'''
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
... | 75 |
"""simple docstring"""
from __future__ import annotations
import math
def lowercase (snake_case__ : int ) -> list[int]:
'''simple docstring'''
if num <= 0:
lowerCAmelCase = f'''{num}: Invalid input, please enter a positive integer.'''
raise Value... | 169 | 0 |
"""simple docstring"""
import numpy
class _A :
"""simple docstring"""
def __init__( self : List[str] , __UpperCAmelCase : numpy.ndarray , __UpperCAmelCase : numpy.ndarray):
a : Optional[... | 135 |
"""simple docstring"""
import argparse
import json
import os
import torch
from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def lowercase ( A_ , A_ , ... | 135 | 1 |
"""simple docstring"""
from typing import List
from .keymap import KEYMAP, get_character
def lowerCamelCase__ ( UpperCAmelCase_ )-> Optional[Any]:
"""simple docstring"""
def decorator(UpperCAmelCase_ ):
UpperCamelCase = g... | 554 |
"""simple docstring"""
class __a :
def __init__( self : Any , UpperCAmelCase_ : List[str] , UpperCAmelCase_ : Optional[Any] , UpperCAmelCase_ : Optional[Any] )-> Optional[int]:
"""simple docstring"""
... | 554 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a= logging.get_logger(__name__)
a= {
'''sayakpaul/vit-msn-base''': '''https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json''',
# See all ViT MSN models at https://huggingface.co/models?... | 714 | '''simple docstring'''
class __lowercase :
"""simple docstring"""
def __init__( self ):
__UpperCamelCase : Any = 0
__UpperCamelCase : Any = 0
__UpperCamelCase : Any = {}
def lowerCAmelCase ( self , _lowerCamelCase ):
... | 287 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCamelCase = logging.get_logger(__name__)
__lowerCamelCase = {
'google/vivit-b-16x2-kinetics400': (
'https://huggingface.co/google/vivit-b-16x2-kine... | 96 |
"""simple docstring"""
def a ( __UpperCAmelCase : int = 1_0_0 ) -> int:
__magic_name__: str = 0
__magic_name__: Any = 0
for i in range(1 , n + 1 ):
sum_of_squares += i**2
sum_of_ints += i
... | 96 | 1 |
"""simple docstring"""
import copy
import inspect
import unittest
from transformers import PretrainedConfig, SwiftFormerConfig
from transformers.testing_utils import (
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_v... | 708 |
"""simple docstring"""
import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
... | 302 | 0 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class __lowerCAmelCase ( a__ ):
"""simple docstring"""
A__ : List[Any] = ["image_processor", "tokenizer"]
A__ : Optional[int] = ... | 9 |
import numpy as np
# Importing the Keras libraries and packages
import tensorflow as tf
from tensorflow.keras import layers, models
if __name__ == "__main__":
# Initialising the CNN
# (Sequential- Building the model layer by layer)
__UpperCAmelCase = models.Sequential()
# Step 1 - Convoluti... | 40 | 0 |
import argparse
import torch
from transformers import FunnelBaseModel, FunnelConfig, FunnelModel, load_tf_weights_in_funnel
from transformers.utils import logging
logging.set_verbosity_info()
def SCREAMING_SNAKE_CASE ( lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , ... | 105 |
from tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch
from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER, require_tf, require_torch
@require_torch
@... | 105 | 1 |
'''simple docstring'''
import torch
from diffusers import DDPMScheduler
from .test_schedulers import SchedulerCommonTest
class UpperCamelCase__ ( a_ ):
"""simple docstring"""
UpperCAmelCase__ = (DDPMScheduler,)
def snake_case ( self : List[str] ... | 497 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
lowerCamelCase : List[Any] = {
'''configuration_owlvit... | 367 | 0 |
'''simple docstring'''
from __future__ import annotations
from dataclasses import dataclass
@dataclass
class a__ :
"""simple docstring"""
__UpperCamelCase : float
__UpperCamelCase : TreeNode | None = None
__UpperCamelCase : TreeNode | None =... | 474 |
'''simple docstring'''
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils imp... | 474 | 1 |
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... | 604 | def __UpperCAmelCase ( UpperCAmelCase = 50 )-> int:
"""simple docstring"""
lowercase = [[0] * 3 for _ in range(length + 1 )]
for row_length in range(length + 1 ):
for tile_length in range(2, 5 ):
for tile_start i... | 604 | 1 |
from __future__ import annotations
from cmath import sqrt
def UpperCAmelCase ( _lowerCamelCase : int , _lowerCamelCase : int , _lowerCamelCase : int ):
'''simple docstring'''
if a == 0:
raise ValueError("Coefficient 'a' must not ... | 26 |
def UpperCAmelCase ( _lowerCamelCase : int = 1_000 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Dict = -1
SCREAMING_SNAKE_CASE__ : str = 0
for a in range(1 , n // 3 ):
# Solving the two equations a**2+b**2... | 26 | 1 |
"""simple docstring"""
from datetime import datetime
import requests
from bsa import BeautifulSoup
if __name__ == "__main__":
__SCREAMING_SNAKE_CASE = input('Enter image url: ').strip()
print(F"""Downloading image from {url} ...""")
__SCREAMING_SNAKE_CASE = BeautifulSoup(requests.get(url... | 357 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version(">=", "4.25.0")):
raise OptionalDe... | 63 | 0 |
'''simple docstring'''
import argparse
import logging
import pickle
from collections import Counter
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt='%m/%d/%Y %H:%M:%S', level=logging.INFO
)
a : List[str] = logging.getLogger(_... | 712 |
'''simple docstring'''
from collections import defaultdict
def __magic_name__ ( __UpperCAmelCase ) -> int:
'''simple docstring'''
snake_case_ = 1
snake_case_ = True
for v in tree[start]:
if v not in visited:
ret += dfs(__UpperCAm... | 593 | 0 |
"""simple docstring"""
from collections import OrderedDict
from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = ... | 88 |
"""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
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = ... | 88 | 1 |
import unittest
from transformers import BigBirdTokenizer, BigBirdTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
_lowerC... | 604 |
from __future__ import annotations
import os
import tempfile
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import is_tensorflow_text_available, is_tf_available
from transformers.testing_utils import require_tensorflow_text, require_tf, slow
from ..test_modeling... | 604 | 1 |
'''simple docstring'''
def _SCREAMING_SNAKE_CASE (A ) -> list:
"""simple docstring"""
if len(A ) <= 1:
return [tuple(A )]
lowercase__ = []
def generate(A , A ):
lowercase__ = [0] * n
res.append(... | 460 |
'''simple docstring'''
from typing import Optional, Tuple
import jax
import jax.numpy as jnp
from flax import linen as nn
from flax.core.frozen_dict import FrozenDict
from transformers import CLIPConfig, FlaxPreTrainedModel
from transformers.models.clip.modeling_flax_clip import FlaxCLIPVisionModule
def _SCREAMIN... | 460 | 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",
"merges_file":... | 13 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase ) -> bool:
'''simple docstring'''
if num < 0:
return False
__SCREAMING_SNAKE_CASE = num
__SCREAMING_SNAKE_CASE = 0
while num > 0:
__SCREAMING_SNAKE_... | 13 | 1 |
import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union
import numpy as np
import torch
from .utils import deprecate, is_transformers_available
if is_transformers_available():
import transformers
def _lowerCAmelCase ( A__: int ... | 254 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = {"""openai-gpt""": """https://huggingface.co/openai-gpt/resolve/main/config.json"""}
class lowercase ( lowercase_... | 535 | 0 |
'''simple docstring'''
import argparse
import json
import os
import sys
import tempfile
import unittest
from argparse import Namespace
from dataclasses import dataclass, field
from enum import Enum
from pathlib import Path
from typing import List, Literal, Optional
import yaml
from transformers import HfArgu... | 708 |
'''simple docstring'''
from __future__ import annotations
import math
def lowerCamelCase__ ( __lowerCamelCase : int , __lowerCamelCase : int , __lowerCamelCase : bool , __lowerCamelCase : list[int] , __lowerCamelCase : float ):
'''simple d... | 331 | 0 |
'''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 | '''simple docstring'''
def lowerCamelCase ( UpperCAmelCase__ : str , UpperCAmelCase__ : int ) -> str:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ :list[list[str]] = [[] for _ in range(UpperCAmelCase__ )]
SCREAMING_SNAKE_CASE__ :Any ... | 209 | 0 |
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,
to_channel_dimension_for... | 495 |
lowerCamelCase__ : Any = """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_... | 495 | 1 |
'''simple docstring'''
import json
import os
from typing import Optional
import numpy as np
from ...feature_extraction_utils import BatchFeature
from ...processing_utils import ProcessorMixin
from ...utils import logging
from ...utils.hub import get_file_from_repo
from ..auto import AutoTokenizer
__lowerCAme... | 697 |
'''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
__lowerCAmelCase =logging.get_logger(__name__)
__lowerCAmelCase ={
"go... | 697 | 1 |
'''simple docstring'''
import argparse
import logging
import pickle
from collections import Counter
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt='%m/%d/%Y %H:%M:%S', level=logging.INFO
)
a__ : Optional[int] = logging.getLogger(__name__)
if __name__ =... | 570 |
'''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.uti... | 570 | 1 |
'''simple docstring'''
UpperCamelCase__ : Optional[Any] = [0, 2, 4, 6, 8]
UpperCamelCase__ : List[str] = [1, 3, 5, 7, 9]
def __UpperCamelCase( _A : int , _A : int , _A : list[int] , _A : int ):
'''simple docstring'''
... | 614 | '''simple docstring'''
import argparse
import logging
import pickle
import random
import time
import numpy as np
from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt='%m/%d/%Y %H:%M:%S', level=logging... | 614 | 1 |
import dataclasses
import re
import string
from typing import Any, Dict, Iterator, List, Mapping, Optional, Sequence, Tuple
import numpy as np
from . import residue_constants
UpperCamelCase = Mapping[str, np.ndarray]
UpperCamelCase = Mapping[str, Any] # Is a nested dict.
UpperCamelCas... | 387 | import random
import torch
from huggingface_hub import HfApi
from diffusers import UNetaDModel
UpperCamelCase = HfApi()
UpperCamelCase = {}
# fmt: off
UpperCamelCase = torch.tensor([
-0.7515, -1.6883, 0.2420, 0.0300, 0.6347, 1.3433, -1.1743, -3.7467,
1.2342, -2.2485... | 387 | 1 |
import warnings
from diffusers import StableDiffusionImgaImgPipeline # noqa F401
warnings.warn(
'The `image_to_image.py` script is outdated. Please use directly `from diffusers import'
' StableDiffusionImg2ImgPipeline` instead.'
) | 670 | import unittest
from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTeste... | 670 | 1 |
def _snake_case (_snake_case : list[int] , _snake_case : int) -> bool:
_lowercase =len(_snake_case)
_lowercase =[[False] * (required_sum + 1) for _ in range(arr_len + 1)]
# for each arr value, a sum of zero(0) can be formed by not taking any el... | 557 |
import argparse
import fairseq
import torch
from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging
logging.set_verbosity_info()
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE = {
"post_extract_proj": "feature... | 557 | 1 |
"""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, TableTransformer... | 453 |
"""simple docstring"""
def SCREAMING_SNAKE_CASE ( lowercase__ , lowercase__ ) -> list:
lowerCAmelCase__ : List[str] = len(lowercase__ )
lowerCAmelCase__ : Dict = []
for i in range(len(lowercase__ ) - pat_len + 1 ):
lowerCAmelCase__ : Union[str, Any] ... | 453 | 1 |
'''simple docstring'''
__A : int = "ABCDEFGHIJKLMNOPQRSTUVWXYZ"
def __UpperCamelCase ( ) ->None:
"""simple docstring"""
lowerCamelCase_ =input("""Enter message: """ )
lowerCamelCase_ =input("""Enter key [alphanumeric]: """ )
lowerCame... | 708 |
def __UpperCamelCase ( _A : str , _A : int ) ->str:
"""simple docstring"""
lowerCamelCase_ =[[] for _ in range(_A )]
lowerCamelCase_ =key - 1
if key <= 0:
raise ValueError("""Height of grid can't be 0 or negative""... | 75 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_UpperCAmelCase : Optional[Any] = {
'''configuration_clipseg''': [
'''CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''CLIPSegConfig'... | 107 |
'''simple docstring'''
from copy import deepcopy
from typing import Optional, Union
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available
if is_torch_available():
... | 211 | 0 |
def lowerCamelCase ( )-> Any:
"""simple docstring"""
a =0
for i in range(1 , 1001 ):
total += i**i
return str(UpperCAmelCase_ )[-10:]
if __name__ == "__main__":
print(solution())
| 715 |
import pytest
from datasets.utils.sharding import _distribute_shards, _number_of_shards_in_gen_kwargs, _split_gen_kwargs
@pytest.mark.parametrize(
"""kwargs, expected""" , [
({"""num_shards""": 0, """max_num_jobs""": 1}, []),
({"""num_shards""": 10, """max_num_jobs""": 1}, [range... | 321 | 0 |
"""simple docstring"""
from typing import Union
import fire
import torch
from tqdm import tqdm
def a ( __UpperCAmelCase : Optional[Any] , __UpperCAmelCase : Dict = "cpu" , __UpperCAmelCase : List[str] = None ) -> None:
... | 96 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ConvNextConfig, SegformerImageProcessor, UperNetConfig, UperNetForSemanticSegmentation
def A_ ( A__ ) -> Dict:
a__ : Dict ... | 302 | 0 |
import unittest
from transformers import GPTNeoXJapaneseConfig, is_torch_available
from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTes... | 387 | # 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 a... | 387 | 1 |
'''simple docstring'''
import argparse
import torch
from transformers import (
SpeechTaConfig,
SpeechTaFeatureExtractor,
SpeechTaForSpeechToSpeech,
SpeechTaForSpeechToText,
SpeechTaForTextToSpeech,
SpeechTaProcessor,
SpeechTaTokenizer,
logging,
)
from tra... | 28 |
'''simple docstring'''
def lowercase__( __UpperCamelCase: int = 1_00_00_00 ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = [i - 1 for i in range(limit + 1 )]
for i in range(2 ,limit + 1 ):
if phi[i] == ... | 28 | 1 |
"""simple docstring"""
import math
def _UpperCamelCase ( _A ) -> bool:
"""simple docstring"""
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 ... | 19 |
"""simple docstring"""
import re
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class a_ ( _UpperCAmelCase ):
a : Any = ['image_processor', 'tokenizer']
a : Optional[int] = 'AutoImageProcessor'
a : An... | 19 | 1 |
import torch
from diffusers import StableDiffusionPipeline
_lowerCAmelCase : List[Any] = '''path-to-your-trained-model'''
_lowerCAmelCase : Any = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to('''cuda''')
_lowerCAmelCase : Tuple... | 454 |
def __snake_case ( _lowerCAmelCase : int = 1 , _lowerCAmelCase : int = 1000 ) -> int:
A_ : Optional[int] = 1
A_ : int = 0
for divide_by_number in range(_lowerCAmelCase , digit + 1 ):
A_ : list[i... | 454 | 1 |
def _SCREAMING_SNAKE_CASE ( a ) -> list:
__A : Optional[int] = [0] * len(a )
for i in range(1 , len(a ) ):
# use last results for better performance - dynamic programming
__A : Optional[int] = prefix_result[i - 1]
whi... | 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 |
def A_ ( A__ ) -> list:
if len(UpperCamelCase__ ) <= 1:
return [tuple(UpperCamelCase__ )]
a__ : List[Any] = []
def generate(A__ , A__ ):
if k == 1:
res.append(tuple(arr[:] ... | 302 |
import math
from datetime import datetime, timedelta
def _A( UpperCamelCase__ : int ) -> datetime:
'''simple docstring'''
__lowercase = year % 19
__lowercase = year % 4
__lowercase = year % 7
__lowercase = math.fl... | 332 | 0 |
'''simple docstring'''
def _UpperCAmelCase ( __A : int ):
if not isinstance(__A , __A ):
raise TypeError('''Input value must be an \'int\' type''' )
a_ : Tuple = 0
while number:
position += 1
... | 702 |
'''simple docstring'''
from unittest.mock import patch
import pyspark
from datasets.packaged_modules.spark.spark import (
Spark,
SparkExamplesIterable,
_generate_iterable_examples,
)
from ..utils import (
require_dill_gt_0_3_2,
require_not_windows,
)
... | 666 | 0 |
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