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'''
def snake_case_ (UpperCamelCase : list[list[int]] , UpperCamelCase : int , UpperCamelCase : int , UpperCamelCase : set ):
'''simple docstring'''
_a , _a = len(UpperCamelCase ), len(grid[0] )... | 22 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
_UpperCAmelCase : Dict = logging.get_logger(__name__)
_UpperCAmelCase : Op... | 668 | 0 |
def _lowerCAmelCase( __A ):
UpperCAmelCase = len(__A )
UpperCAmelCase = len(matrix[0] )
UpperCAmelCase = min(__A , __A )
for row in range(__A ):
# Check if diagonal element is not zero
if matrix[row][row] != 0:
# Eliminate all the elements... | 702 |
import numpy as np
from nltk.translate import meteor_score
import datasets
from datasets.config import importlib_metadata, version
lowerCAmelCase__ = version.parse(importlib_metadata.version("nltk"))
if NLTK_VERSION >= version.Version("3.6.4"):
from nltk import word_tokenize
lowerCAmelCase__ = ... | 1 | 0 |
'''simple docstring'''
import argparse
import json
import os
from collections import OrderedDict
import torch
from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def __lowercase (_SCREAMING... | 507 |
# Usage:
# ./gen-card-allenai-wmt16.py
import os
from pathlib import Path
def lowerCamelCase_ ( UpperCamelCase__ : int , UpperCamelCase__ : Optional[int] , UpperCamelCase__ : int , UpperCamelCase__ : str ) -> List[str]:
"""si... | 469 | 0 |
'''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__)
__lowerCamelC... | 719 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_beit import BeitImageProcessor
__lowerCamelCase = logging.get_logger(__name__)
class A__ ( _snake_case ):
def __init__( self , *UpperCamelCase__ , **UpperCamelCase__ ... | 667 | 0 |
'''simple docstring'''
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartaaTokenizer, MBartaaTokenizerFast, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece... | 90 |
'''simple docstring'''
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device
__UpperCAmelCase = False
... | 90 | 1 |
'''simple docstring'''
import numpy as np
import qiskit
def lowerCamelCase ( UpperCAmelCase__ : int = 8 , UpperCAmelCase__ : int | None = None ) -> str:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ :Tuple = np.random.default_rng(seed=Upper... | 320 | '''simple docstring'''
from typing import Optional
from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
class _SCREAMING_SNAKE_CASE( _SCREAMING_SNAKE_CASE ):
de... | 320 | 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_ : List[str] = {
'''configuration_roberta''': [... | 24 |
'''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,
)
def _UpperCamelCase (_l... | 24 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__magic_name__ = {
'''configuration_ctrl''': ['''CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CTRLConfig'''],
'''tokenization_ctrl''': ['''CTRLTokenize... | 314 |
__magic_name__ = range(2, 20 + 1)
__magic_name__ = [10**k for k in range(ks[-1] + 1)]
__magic_name__ = {}
def __snake_case ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
"""simple docstring"""
... | 314 | 1 |
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
logging.set_ver... | 339 |
from math import factorial
def _a ( UpperCamelCase_ : int = 20 ) -> int:
"""simple docstring"""
lowerCAmelCase__ = 2 * n # middle entry of odd rows starting at row 3 is the solution for n = 1,
# 2, 3,...
lowerCAmelCase__ = n // 2
return... | 339 | 1 |
import argparse
from typing import List
import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset
# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
from torch.optim import AdamW
from torch.utils.data import Dat... | 548 |
import unittest
from transformers import (
MODEL_FOR_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_CAUSAL_LM_MAPPING,
TextGenerationPipeline,
logging,
pipeline,
)
from transformers.testing_utils import (
CaptureLogger,
is_pipeline_test,
require_accelerate,
require_tf,
require_torch,
... | 548 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
UpperCAmelCase_ : Optional[Any] = {
'''configuration_altclip''': [
'''ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''AltCLIPConfig''',
... | 17 |
"""simple docstring"""
import json
import os
import unittest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@requ... | 46 | 0 |
from __future__ import annotations
def _SCREAMING_SNAKE_CASE ( snake_case_ : Any , snake_case_ : Tuple ):
__magic_name__ , __magic_name__ = set(_SCREAMING_SNAKE_CASE ), [start]
while stack:
__magic_name__ = stack.pop()
explored.add(_SCREAMING_SN... | 702 |
import argparse
import requests
import torch
from PIL import Image
from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor
def _SCREAMING_SNAKE_CASE ( snake_case_ : Optional[Any] ):
__magic_name__ = SwinConfig(image_size=192 )
if "base" in model_name:
... | 678 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
a :List[Any] = {
"configuration_mobilebert": [
"MOBILEBERT_PRETRAINED_CONFIG_ARCH... | 680 |
"""simple docstring"""
def _lowercase ( __lowerCAmelCase = 200_0000 ) -> int:
SCREAMING_SNAKE_CASE__ : int = [0 for i in range(n + 1 )]
SCREAMING_SNAKE_CASE__ : str = 1
SCREAMING_SNAKE_CASE__ : str = 1
for i in range(2 , in... | 680 | 1 |
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 impor... | 675 |
def __SCREAMING_SNAKE_CASE ( ) -> list[list[int]]:
'''simple docstring'''
return [list(range(1000 - i , -1000 - i , -1 ) ) for i in range(1000 )]
lowerCAmelCase = generate_large_matrix()
lowerCAmelCase = (
[[4, 3, 2, -1], [3, 2,... | 675 | 1 |
import hashlib
import unittest
from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_tim... | 419 |
import json
import os
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
SCREAMING_SNAKE_CASE : List[str] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE : Any = {
"vocab_file": "vocab.json",
... | 419 | 1 |
import os
from distutils.util import strtobool
def __UpperCAmelCase( lowercase_ , lowercase_ ):
for e in env_keys:
_lowerCamelCase : List[str] = int(os.environ.get(lowercase_ , -1 ) )
if val >= 0:
return val
... | 613 |
import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple
import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ner import Split, ... | 613 | 1 |
import inspect
import os
import unittest
from pathlib import Path
import torch
import accelerate
from accelerate.test_utils import execute_subprocess_async
from accelerate.test_utils.testing import run_command
class __UpperCamelCase ( unittest.TestCase ):
__A : List[Any] = ins... | 32 |
'''simple docstring'''
__UpperCAmelCase = """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... | 379 | 0 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepi... | 713 |
'''simple docstring'''
import argparse
import json
import os
import re
import torch
from transformers import BloomConfig, BloomModel
from transformers.file_utils import CONFIG_NAME, WEIGHTS_NAME
from transformers.utils import logging
logging.set_verbosity_info()
UpperCamelCase_ ... | 508 | 0 |
'''simple docstring'''
import argparse
import os
import re
__lowerCamelCase = '''src/transformers/models/auto'''
# re pattern that matches mapping introductions:
# SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
__lowerCamelCase = re.compile(R'... | 467 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class UpperCAmelCase ( metaclass=_snake_case ):
UpperCAmelCase = ["speech"]
def __init__( self : List[Any] , *__lowerCamelCase : List[Any] , **__lowerCamelCase : List[str]... | 467 | 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_camembert i... | 106 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__UpperCamelCase : Tuple = logging.get_logger(__name__)
__UpperCamelCase... | 106 | 1 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Iterator
class lowerCAmelCase__ :
'''simple docstring'''
def __init__( self , lowercase ):
_lowerCamelCase : Union[str, Any] = valu... | 630 |
"""simple docstring"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
... | 630 | 1 |
def snake_case ():
'''simple docstring'''
lowerCamelCase__ = []
lowerCamelCase__ = 1
while len(_lowerCAmelCase ) < 1e6:
constant.append(str(_lowerCAmelCase ) )
i += 1
lowerCamelCase__ = "".join(_lowerCAmelCase )
return (
int(con... | 716 |
from string import ascii_lowercase, ascii_uppercase
def snake_case (UpperCamelCase : str ):
'''simple docstring'''
if not sentence:
return ""
lowerCamelCase__ = dict(zip(UpperCamelCase , UpperCamelCase ) )
return lower_to_upper.get(sentence[0] , ... | 235 | 0 |
def lowerCAmelCase_ ( __a ) -> int:
"""simple docstring"""
if not numbers:
return 0
if not isinstance(__a , (list, tuple) ) or not all(
isinstance(__a , __a ) for number in numbers ):... | 258 |
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def lowerCAmelCase_ ( ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Any =HfArgumentParser(__a )
SCREAMING_SNAKE_CA... | 258 | 1 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, ClassLabel, Features
from .base import TaskTemplate
@dataclass(frozen=a__ )
class UpperCAmelCase__ ( a__ ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE ... | 710 |
from itertools import zip_longest
import requests
from bsa import BeautifulSoup
from pandas import DataFrame
def lowerCamelCase ( UpperCAmelCase_ : str = "laptop" )-> DataFrame:
"""simple docstring"""
a =F'''https://www.amazon.in/laptop/s?k={product}'''
... | 321 | 0 |
from __future__ import annotations
from collections.abc import Iterator
class A__ :
def __init__( self : Any , _a : int ) -> None:
"""simple docstring"""
_SCREAMING_SNAKE_CASE =value
_SCREAMING_SNAKE_CASE =None
... | 691 |
# Usage:
# ./gen-card-allenai-wmt16.py
import os
from pathlib import Path
def lowerCamelCase( a__ ,a__ ,a__ ,a__):
_SCREAMING_SNAKE_CASE ={
'''en''': '''Machine learning is great, isn\'t it?''',
'''ru''': '''Машинное обучение - это здорово, не так ли?''',
... | 691 | 1 |
"""simple docstring"""
from __future__ import annotations
class snake_case :
"""simple docstring"""
def __init__( self : Dict ,lowerCamelCase__ : list[list[int]] ):
UpperCAmelCase__ = TypeError(
'Matrices must be formed... | 702 | """simple docstring"""
from typing import Optional
import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixi... | 632 | 0 |
"""simple docstring"""
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
from .timesteps import (
fastaa_timesteps,
smartaa_timesteps,
... | 4 |
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 datasets.config import MA... | 47 | 0 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as t... | 713 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import torch
import torchaudio
from datasets import load_dataset
from huggingface_hub import hf_hub_download
from transformers import ASTConfig, ASTFeatureExtractor, ASTForAudioClassification
from transformers.u... | 168 | 0 |
'''simple docstring'''
import os
from collections import namedtuple
import pytest
from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict
_SCREAMING_SNAKE_CASE = namedtuple(
"_TestCommand... | 18 |
from collections.abc import Iterable
from typing import Generic, TypeVar
UpperCamelCase = TypeVar('_T')
class _A ( Generic[_T] ):
def __init__( self : int , lowerCamelCase__ : Iterable[_T] | None = None ):
"""simple docstring"""
__UpperCamelCase : ... | 269 | 0 |
"""simple docstring"""
# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position
A_ : Tuple = "2.13.1"
import platform
import pyarrow
from packaging import version
if version.parse(platform.python_vers... | 616 |
"""simple docstring"""
import argparse
import logging
from collections import namedtuple
import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer # The authors' implementation
from transformers import BertTokenizer
logging.basicConfig(level=log... | 616 | 1 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
UniSpeechConfig,
UniSpeechForCTC,
UniSpeechForPreTraining,
WavaVecaFeatureExtractor,
WavaVecaPhonemeCTCTokenizer,
WavaVecaProcessor,
... | 474 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case : Optional[int] = logging.get_logger(__name__)
snake_case : Optional[int] = {
'''microsoft/unispeech-large-1500h-cv''': (
'''https://h... | 445 | 0 |
import math
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : Dict) -> Tuple:
'''simple docstring'''
__UpperCamelCase : Any = [True] * n
__UpperCamelCase : Optional[int] = False
__UpperCamelCase : List[str] ... | 716 |
import argparse
import torch
from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging
logging.set_verbosity_info()
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : Dict , _lowerCamelCase : Tuple , _lowerCa... | 94 | 0 |
from __future__ import annotations
from typing import Any
class lowerCamelCase_ ( _lowercase ):
pass
class lowerCamelCase_ :
def __init__( self : Optional[int] , __A : Any ):
__A : Any = data
__A : Node | None = None
... | 17 |
from collections import namedtuple
import requests
from lxml import html # type: ignore
SCREAMING_SNAKE_CASE = namedtuple("covid_data", "cases deaths recovered")
def snake_case__ ( __SCREAMING_SNAKE_CASE = "https://www.worldometers.info/coronavirus/" ) -> covid_data:
UpperCAmelCase_ ... | 579 | 0 |
'''simple docstring'''
from __future__ import annotations
from typing import Any
class lowerCamelCase_ ( snake_case_ ):
pass
class lowerCamelCase_ :
def __init__( self : Union[str, Any] , lowerCAmelCase__ : Any ):
... | 464 |
'''simple docstring'''
import math
import unittest
from transformers import BioGptConfig, 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
... | 464 | 1 |
import uuid
from typing import Any, Dict, List, Optional, Union
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
if is_torch_available():
import torch
lo... | 154 |
from typing import List, Optional, TypeVar
from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_dat... | 154 | 1 |
'''simple docstring'''
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
lowercase =logging.get_logger(__name__)
l... | 716 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase =logging.get_logger(__name__)
lowercase ={
'facebook/dpr-ctx_encoder-single-nq-base': (
'https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/confi... | 331 | 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,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimen... | 220 |
import logging
from transformers.configuration_utils import PretrainedConfig
__SCREAMING_SNAKE_CASE = logging.getLogger(__name__)
class lowerCAmelCase_ ( __A ):
'''simple docstring'''
_lowercase = 'masked_bert'
def __init__( self , ... | 220 | 1 |
import warnings
from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor
_lowerCAmelCase = logging.get_logger(__name__)
class lowerCAmelCase_ ( __lowercase ):
def __init__( self : Tuple , *_A : Union[str, Any] , **_A : Opti... | 71 | import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
if is_torch_available():
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
@require_torch
@require_sentencepiece
@require_to... | 71 | 1 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, loggin... | 561 |
import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image
from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_proces... | 493 | 0 |
UpperCamelCase = 8.3_144_598
def __lowerCamelCase ( snake_case__ ,snake_case__ ) -> float:
"""simple docstring"""
if temperature < 0:
raise Exception("""Temperature cannot be less than 0 K""" )
if molar_mass <= 0:
raise Exception(""... | 702 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky... | 569 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase_ : Optional[int] = {
'configuration_trajectory_transformer': [
'TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG... | 527 |
"""simple docstring"""
import inspect
import unittest
from typing import List
import numpy as np
from transformers import EfficientFormerConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...t... | 621 | 0 |
'''simple docstring'''
import importlib.metadata
from typing import Union
from packaging.version import Version, parse
from .constants import STR_OPERATION_TO_FUNC
SCREAMING_SNAKE_CASE = parse(importlib.metadata.version('torch'))
def lowercase_ ( __A : Union[str, Version] , ... | 8 |
'''simple docstring'''
import importlib.metadata
from typing import Union
from packaging.version import Version, parse
from .constants import STR_OPERATION_TO_FUNC
SCREAMING_SNAKE_CASE = parse(importlib.metadata.version('torch'))
def lowercase_ ( __A : Union[str, Version] , ... | 8 | 1 |
import re
import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey
from ..utils import logging
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
def _snake_case (_snake_case : Union[str, Any]) -> int:
_lowercase ... | 181 |
"""simple docstring"""
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 ... | 123 | 0 |
UpperCAmelCase_ = {
"A": ["B", "C", "E"],
"B": ["A", "D", "E"],
"C": ["A", "F", "G"],
"D": ["B"],
"E": ["A", "B", "D"],
"F": ["C"],
"G": ["C"],
}
def UpperCAmelCase__ ( _SCREAMING_SNAKE_CASE : dict , _SCREAMING_SNAKE_CASE : List[Any] , _SCREA... | 718 |
def UpperCAmelCase__ ( _SCREAMING_SNAKE_CASE : Optional[Any] )->Any: # noqa: E741
_lowerCAmelCase = len(_SCREAMING_SNAKE_CASE )
_lowerCAmelCase = 0
_lowerCAmelCase = [0] * n
_lowerCAmelCase = [False] * n
_lowerCAmelCase = [False] * n
def d... | 664 | 0 |
import unittest
from transformers import BigBirdConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax
from transformers.models.big... | 70 |
from __future__ import annotations
__lowerCAmelCase = []
def _lowercase ( a__ : list[list[int]] , a__ : int , a__ : int ) -> bool:
"""simple docstring"""
for i in range(len(a__ ) ):
if board[row][i] == 1:
return False
for i ... | 147 | 0 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
... | 701 |
"""simple docstring"""
from itertools import product
def _lowerCamelCase ( _UpperCamelCase , _UpperCamelCase ):
'''simple docstring'''
__lowerCAmelCase = sides_number
__lowerCAmelCase = max_face_number * dice_number
__lowerCAmelCase = [0] * (m... | 282 | 0 |
'''simple docstring'''
from __future__ import annotations
def UpperCamelCase ( lowercase_ : list[list[int]] ) -> bool:
'''simple docstring'''
lowercase =len(_UpperCamelCase )
# We need to create solution object to save path.
lowercase =[[0 for _ in range(_UpperCame... | 72 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart im... | 139 | 0 |
"""simple docstring"""
from typing import Any
def _a ( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , ) -> list:
_validation(
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmel... | 702 |
"""simple docstring"""
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cach... | 690 | 0 |
"""simple docstring"""
from random import shuffle
import tensorflow as tf
from numpy import array
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> str:
SCREAMING_SNAKE_CASE__ : List[str] = int(__lowerCAmelCase )
assert noofclusters < len(__lowe... | 680 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_torch_available,
)
a :Union[str, Any] = {
"configuration_speecht5": [
"SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP",
... | 680 | 1 |
"""simple docstring"""
import json
import os
import re
import unittest
from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common ... | 696 |
"""simple docstring"""
from __future__ import annotations
import math
from collections import Counter
from string import ascii_lowercase
def lowerCamelCase_ ( _lowerCamelCase ):
lowerCamelCase__ , lowerCamelCase__ : List[str] = analyze_text(_lowerCamelCase )
lowerC... | 696 | 1 |
'''simple docstring'''
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class ... | 8 |
'''simple docstring'''
import argparse
import torch
from transformers import (
UniSpeechSatConfig,
UniSpeechSatForAudioFrameClassification,
UniSpeechSatForSequenceClassification,
UniSpeechSatForXVector,
WavaVecaFeatureExtractor,
logging,
)
logging.set_verbosity_info()
a_ = ... | 685 | 0 |
from __future__ import annotations
import unittest
from transformers import DebertaVaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_ma... | 710 | '''simple docstring'''
from typing import List
import jiwer
import jiwer.transforms as tr
from packaging import version
import datasets
from datasets.config import PY_VERSION
if PY_VERSION < version.parse("""3.8"""):
import importlib_metadata
else:
import importlib.metadata as importlib_... | 512 | 0 |
def lowerCamelCase_ ( UpperCAmelCase__ , UpperCAmelCase__ ):
"""simple docstring"""
return [sentence[i : i + ngram_size] for i in range(len(UpperCAmelCase__ ) - ngram_size + 1 )]
if __name__ == "__main__":
from doctest import testmod
testmod() | 483 |
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.stable_di... | 483 | 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 load_image
if is_torch... | 321 |
_lowerCamelCase = '''ABCDEFGHIJKLMNOPQRSTUVWXYZ'''
def lowerCamelCase ( )-> None:
"""simple docstring"""
a =input("""Enter message: """ )
a =input("""Enter key [alphanumeric]: """ )
a =input("""Encrypt/Decrypt [e/d]: """ )
... | 321 | 1 |
from __future__ import annotations
import math
from collections.abc import Callable
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = 100 , ) -> float:
snake_case__ = x_start
snake_case__ ... | 33 |
from __future__ import annotations
import unittest
from transformers import BlenderbotConfig, BlenderbotTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTe... | 33 | 1 |
from typing import List, Optional, Union
import numpy as np
import PIL.Image
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
PILImageResampling,
get_imag... | 706 |
from __future__ import annotations
from scipy.special import comb # type: ignore
class lowercase :
"""simple docstring"""
def __init__( self : Optional[int] , a_ : list[tuple[float, float]] ):
"""simple docstring"""
lowerCamelCase__ =... | 235 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
import diffusers
from diffusers import (
AutoencoderKL,
EulerDiscreteScheduler,
StableDiffusionLatentUpscalePipeline,
StableDiffusionPipeli... | 252 |
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
A__ = logging.get_logger(__name__)
A__ = {
'''facebook/data2vec-visio... | 252 | 1 |
"""simple docstring"""
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.text import TextDatasetReader
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def lowerCAmelCase_ ( lowercase_ : List[str... | 401 |
"""simple docstring"""
def lowerCAmelCase_ ( lowercase_ : Union[str, Any] , lowercase_ : List[str] , lowercase_ : Tuple , lowercase_ : int , lowercase_ : int , lowercase_ : Optional[int] ):
'''simple docstring'''
... | 401 | 1 |
'''simple docstring'''
import json
import os
import pickle
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from datasets import Dataset
from transformers import is_faiss_available
from transformers.models.bart.configuration_bart import Bart... | 274 |
'''simple docstring'''
from transformers import BertTokenizerFast
from .custom_tokenization import CustomTokenizer
class _SCREAMING_SNAKE_CASE ( UpperCamelCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ = CustomTokenizer
pass
| 316 | 0 |
import argparse
import logging
from collections import namedtuple
import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer # The authors' implementation
from transformers import BertTokenizer
logging.basicConfig(level=logging.INFO)
snake_case__ : Dict = ... | 592 |
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 _a ( unittest.TestCase ):
"""simple docstring"""
def SCREAMING_SNAK... | 592 | 1 |
import collections
import json
import os
import re
from typing import TYPE_CHECKING, List, Optional, Tuple
import numpy as np
from ...tokenization_utils_fast import PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
a_ :Tuple ... | 35 |
import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union
from filelock import FileLock
from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available
a_ :List[Any] = logging.getLogger(__name__)
@dataclass
class ... | 35 | 1 |
"""simple docstring"""
def _snake_case ( UpperCAmelCase_ : int ):
A__ = [1]
A__ = 0, 0, 0
A__ = ugly_nums[ia] * 2
A__ = ugly_nums[ia] * 3
A__ = ugly_nums[ia] * 5
for _ in range(1 , snake_case__ ):... | 706 |
"""simple docstring"""
import inspect
import unittest
from transformers import SegformerConfig, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import Co... | 500 | 0 |
import math
def snake_case_ ( SCREAMING_SNAKE_CASE_ ) -> bool:
return math.sqrt(lowerCAmelCase__ ) * math.sqrt(lowerCAmelCase__ ) == num
def snake_case_ ( SCREAMING_SNAKE_CASE_ ) -> bool:
lowercase__ : Optional[Any] = 0
... | 397 |
"""simple docstring"""
def lowercase__ ( lowerCAmelCase__ : str ) -> list[int]:
'''simple docstring'''
a__ : List[str] = [0 for i in range(len(lowerCAmelCase__ ) )]
# initialize interval's left pointer and right pointer
a__ , a__ : int = 0, 0
for i in ra... | 642 | 0 |
"""simple docstring"""
def snake_case__ ( __lowerCamelCase : str , __lowerCamelCase : bool = False ):
"""simple docstring"""
if not isinstance(__lowerCamelCase , __lowerCamelCase ):
lowerCamelCase__ : Union[str, Any] =f'''Expected string as input, foun... | 625 |
"""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, DDIMSche... | 625 | 1 |
'''simple docstring'''
from typing import Any, Dict, List, Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_... | 72 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class a_ ( _a ):
a : Optional[Any] = ['''image_processor''', '''tokenizer''']
a : Optional[Any] = '''ChineseCLIPImageProcessor'''
a : List[str] ... | 287 | 0 |
from __future__ import annotations
def snake_case__ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->list[tuple[int, int]]:
UpperCAmelCase__ , UpperCAmelCase__ = position
UpperCAmelCase__ = [
(y + 1, x + 2),
(y - 1, x + 2),
(y + 1, x - 2),
(y - 1... | 720 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_... | 422 | 0 |
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.... | 151 |
'''simple docstring'''
import argparse
import torch
from datasets import load_dataset
from donut import DonutModel
from transformers import (
DonutImageProcessor,
DonutProcessor,
DonutSwinConfig,
DonutSwinModel,
MBartConfig,
MBartForCausalLM,
VisionEncoderDecoderModel,
XLMRoberta... | 588 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCAmelCase : Optional[Any] = logging.get_logger(__name__)
UpperCAmelCase : int = {
"fa... | 714 | """simple docstring"""
UpperCAmelCase : Tuple = 8.314_462 # Unit - J mol-1 K-1
def __a ( _lowercase , _lowercase , _lowercase ):
"""simple docstring"""
if moles < 0 or kelvin < 0 or volume < 0:
raise ValueError('''Invalid inputs. Enter positiv... | 121 | 0 |
"""simple docstring"""
def _A (__a ) -> int:
"""simple docstring"""
if n == 1 or not isinstance(__a , __a ):
return 0
elif n == 2:
return 1
else:
SCREAMING_SNAKE_CASE_ : Tuple = [0, 1]
for i in range(2 ... | 512 |
"""simple docstring"""
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_ava... | 512 | 1 |
"""simple docstring"""
def a_ ( _lowerCAmelCase : int ):
'''simple docstring'''
lowercase__ : Any = abs(snake_case__ )
lowercase__ : Optional[Any] = 0
while n > 0:
res += n % 10
n //= 10
return r... | 700 | """simple docstring"""
import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor
def a_ ( _lowerCAmelCase : dict ):
... | 645 | 0 |
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
... | 628 |
import torch
from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class __magic_name__ ( snake_case ):
UpperCamelCase_ :Dict = (KDPMaDiscreteScheduler,)
UpperCamelCase_ ... | 628 | 1 |
"""simple docstring"""
from collections import deque
class __lowerCAmelCase :
"""simple docstring"""
def __init__( self : Optional[Any] , _snake_case : str , _snake_case : int , _snake_case : int ) -> None:
... | 702 |
"""simple docstring"""
import hashlib
import unittest
from typing import Dict
import numpy as np
from transformers import (
MODEL_FOR_MASK_GENERATION_MAPPING,
TF_MODEL_FOR_MASK_GENERATION_MAPPING,
is_vision_available,
pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from tra... | 482 | 0 |
def lowerCamelCase__ (_UpperCAmelCase = 50):
SCREAMING_SNAKE_CASE = [[0] * 3 for _ in range(length + 1)]
for row_length in range(length + 1):
for tile_length in range(2 , 5):
for tile_start in range(row_length - tile_length + 1):
different_colour_ways_n... | 73 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
UpperCAmelCase : Any = {
"configuration_roc_bert": ["ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "RoCBertConfig"],
"tokenization_roc_bert": ["RoCBertTokeniz... | 457 | 0 |
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_util... | 712 |
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import numpy as np
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
import transformers
from transf... | 650 | 0 |
'''simple docstring'''
import unittest
from transformers import DebertaVaTokenizer, DebertaVaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
__lowerCAmelCase = get_tests_d... | 229 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging
__lowerCAmelCase =... | 229 | 1 |
"""simple docstring"""
from __future__ import annotations
UpperCamelCase_ : List[Any] = []
def __lowercase ( a : list[list[int]] , a : int , a : int ) -> bool:
for i in range(len(a ) ):
if board[row][i] == 1:
return F... | 701 |
"""simple docstring"""
import importlib.util
import os
import platform
from argparse import ArgumentParser
import huggingface_hub
from .. import __version__ as version
from ..utils import (
is_accelerate_available,
is_flax_available,
is_safetensors_available,
is_tf_available,
is_torch_... | 497 | 0 |
def __snake_case ( __UpperCamelCase : int ):
"""simple docstring"""
if not isinstance(__UpperCamelCase ,__UpperCamelCase ) or number < 0:
raise ValueError("Input must be a non-negative integer" )
A_ = 0
while number:
# ... | 86 |
import argparse
import re
from pathlib import Path
import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor
from transformers import (
EfficientFormerConfig,
EfficientFormerForImageClassificationWithTeacher,
... | 376 | 0 |
'''simple docstring'''
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 lo... | 706 |
'''simple docstring'''
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
... | 427 | 0 |
import torch
from diffusers import DDIMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class __a( _a ):
"""simple docstring"""
lowerCAmelCase = (DDIMParallelScheduler,)
lowerCAmelCase = (('''eta''', 0.0), ('''num_inference_steps''', 50))
de... | 30 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__snake_case : Tuple = logging.get_logger(__name__)
__snake_case : Any = {
'facebook/xglm-564M': 'https://huggingface.co/facebook/xglm-564M/resolve/main/config.j... | 293 | 0 |
'''simple docstring'''
import multiprocessing
from typing import TYPE_CHECKING, Optional, Union
from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream
if TYPE_CHECKING... | 708 |
'''simple docstring'''
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.tokeniza... | 271 | 0 |
from sklearn.metrics import recall_score
import datasets
_lowerCAmelCase = """
Recall is the fraction of the positive examples that were correctly labeled by the model as positive. It can be computed with the equation:
Recall = TP / (TP + FN)
Where TP is the true positives and FN is the false nega... | 137 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCAmelCase = logging.get_logger(__name__)
_lowerCAmelCase = {
"""microsoft/swinv2-tiny-patch4-window8-256""": (
"""https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256/resolve/m... | 137 | 1 |
import numpy as np
def __lowerCAmelCase ( __lowerCamelCase : np.array ) -> np.array:
return 1 / (1 + np.exp(-vector ))
if __name__ == "__main__":
import doctest
doctest.testmod()
| 456 |
def __lowerCAmelCase ( __lowerCamelCase : int = 3 , __lowerCamelCase : int = 7 , __lowerCamelCase : int = 1000000 ) -> int:
__lowerCAmelCase =0
__lowerCAmelCase =1
for current_denominator in range(1 , limit + 1 ):
__lowerCAmelCase =current_deno... | 456 | 1 |
import gc
import importlib.metadata
import tempfile
import unittest
from packaging import version
from transformers import (
AutoModel,
AutoModelForCausalLM,
AutoModelForSeqaSeqLM,
AutoModelForSequenceClassification,
AutoTokenizer,
BitsAndBytesConfig,
pipeline,
)
from... | 1 |
"""simple docstring"""
class _lowerCAmelCase :
def __init__( self , lowerCAmelCase_ , lowerCAmelCase_=None , lowerCAmelCase_=None ) -> int:
_SCREAMING_SNAKE_CASE : Optional[int] = data
_SCREAMING_SNAKE_CASE : Tuple = ... | 621 | 0 |
def lowercase ( __A : list[int] , __A : int ) -> List[Any]:
'''simple docstring'''
snake_case : Optional[int] = len(__A )
snake_case : int = [[False] * (required_sum + 1) for _ in range(arr_len + 1 )]
# for each arr value, a sum ... | 702 |
from __future__ import annotations
from collections import Counter
from random import random
class _A :
'''simple docstring'''
def __init__( self ):
'''simple docstring'''
snake_case : Optional[Any] = {}
def snake_case_ ( self ,SCREAMI... | 315 | 0 |
import warnings
from ...utils import logging
from .image_processing_owlvit import OwlViTImageProcessor
snake_case = logging.get_logger(__name__)
class A_ ( UpperCAmelCase ):
"""simple docstring"""
def __init__( self... | 67 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowercase : Optional[int] = logging.get_logger(__name__)
__lowercase : Optional[int] = {
'''studio-ousia/luke-base''': '''https://huggingface.co/studio-ousia/luke-base/resolve/... | 422 | 0 |
from typing import List
import jiwer
import jiwer.transforms as tr
from packaging import version
import datasets
from datasets.config import PY_VERSION
if PY_VERSION < version.parse("""3.8"""):
import importlib_metadata
else:
import importlib.metadata as importlib_metadata
__a = """"... | 703 |
import numpy
class __lowercase :
def __init__( self : Union[str, Any] , __lowerCamelCase : numpy.ndarray , __lowerCamelCase : numpy.ndarray ) -> None:
"""simple docstring"""
... | 627 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
"bigcode/gpt_bigcode-santacoder": "https://huggingface.co/bigcode/gpt_bigcode-santacoder/resolve/main/config.json",
}
class _SCREAMING_SNAKE_CASE (... | 59 |
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_... | 59 | 1 |
"""simple docstring"""
def lowercase ( UpperCamelCase : list[list[int]] , UpperCamelCase : int , UpperCamelCase : int , UpperCamelCase : list[int] ):
"""simple docstring"""
# 1. Validate that path exists between current and next vertices
... | 595 | """simple docstring"""
import unittest
from diffusers.pipelines.pipeline_utils import is_safetensors_compatible
class __lowerCAmelCase ( unittest.TestCase):
'''simple docstring'''
def _UpperCAmelCase ( self : str ):
A__ : Optional[Any] =[
... | 595 | 1 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
... | 142 |
'''simple docstring'''
import json
import os
import pickle
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from datasets import Dataset
from transformers import is_faiss_available
from transformers.models.bart.configuration_bart import BartConf... | 270 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
import torch
from ..models.auto import AutoModelForVisualQuestionAnswering, AutoProcessor
from ..utils import requires_backends
from .base import PipelineTool
if TYPE_CHECKING:
from PIL import Image
class SCREAMING_SNAKE_CA... | 717 |
'''simple docstring'''
def __lowerCamelCase ( SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int ) -> int:
"""simple docstring"""
return 1 if input_a == input_a else 0
def __lowerCamelCase ( ) -> None:
"""s... | 68 | 0 |
'''simple docstring'''
import gc
import unittest
from diffusers import FlaxStableDiffusionInpaintPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_... | 128 |
'''simple docstring'''
import re
def lowerCAmelCase__ ( lowerCamelCase : str ):
if len(re.findall('[ATCG]' ,lowerCamelCase ) ) != len(lowerCamelCase ):
raise ValueError('Invalid Strand' )
return dna.translate(dna.maketrans('ATCG' ,'TAGC' ) )
if _... | 128 | 1 |
import math
import sys
def __lowerCAmelCase ( __lowerCamelCase : str ) -> str:
__lowerCAmelCase =""""""
try:
with open(__lowerCamelCase , """rb""" ) as binary_file:
__lowerCAmelCase =binary_file.read()
for dat in data:
__lo... | 456 |
def __lowerCAmelCase ( __lowerCamelCase : int ) -> None:
__lowerCAmelCase =generate_pascal_triangle(__lowerCamelCase )
for row_idx in range(__lowerCamelCase ):
# Print left spaces
for _ in range(num_rows - row_idx - 1 ):
print(end=""" """ )
#... | 456 | 1 |
import gc
import unittest
from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_ut... | 43 |
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__ = {
"""kssteven/ibert-roberta-base""": """https://huggingface... | 477 | 0 |
from __future__ import annotations
def __SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ = None , lowercase_ = None ) -> None:
"""simple docstring"""
if start is None:
__UpperCamelCase = 0
if end is None:
__UpperCamelCase = len(l... | 375 |
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 _lowerCamelCase ( _SCREAMING_SNAKE_C... | 375 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase = logging.get_logger(__name__)
UpperCamelCase = {
"sayakpaul/vit-msn-base": "https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json",
# See all ViT MSN models at https://... | 45 |
"""simple docstring"""
import os
import zipfile
import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links
def lowerCamelCase__ ( UpperCAmelCase_ , UpperCAmelCase_=7 )-> Optional[Any]:
"""simple docstring"""
UpperCa... | 554 | 0 |
'''simple docstring'''
import json
import logging
import os
import sys
from pathlib import Path
import finetune_rag
from transformers.file_utils import is_apex_available
from transformers.testing_utils import (
TestCasePlus,
execute_subprocess_async,
require_ray,
require_torch_gpu,
req... | 187 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__A : Tuple = {
"""configuration_deberta""": ["""DEBERTA_PRETRAINED_CONF... | 187 | 1 |
"""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 = {
"microsoft/git-base": "https://huggingface... | 308 |
"""simple docstring"""
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 BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from tra... | 308 | 1 |
"""simple docstring"""
import tempfile
import torch
from diffusers import PNDMScheduler
from .test_schedulers import SchedulerCommonTest
class _SCREAMING_SNAKE_CASE ( _a ):
'''simple docstring'''
__UpperCAmelCase = (PNDMScheduler,)
__UpperCAmelCase = (... | 720 |
"""simple docstring"""
class _SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__(self ):
'''simple docstring'''
_UpperCamelCase : Any = ""
_UpperCamelCase : Union[str, Any] = ""
_UpperCamelCase : Dict = ... | 239 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : Any = logging.get_logger(__name__)
a : Optional[int] = {
'''EleutherAI/gpt-neox-20b''': '''https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/m... | 633 |
"""simple docstring"""
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision import transforms
from transformers import BitImageProcessor, FocalNetConfig, FocalNetForImageClassification
from transforme... | 633 | 1 |
def lowerCAmelCase_ ( __a , __a ) -> List[Any]:
"""simple docstring"""
lowerCamelCase__: List[str] =""
for i in table:
res += inp[i - 1]
return res
def lowerCAmelCase_ ( __a ) -> int:
"""simple docstring"""
return data[1:] + data[0]
de... | 717 |
import inspect
import unittest
from transformers import MobileNetVaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ..... | 437 | 0 |
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