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 |
|---|---|---|---|---|
def lowerCamelCase__ ( _a = 4000000):
SCREAMING_SNAKE_CASE : Optional[int] = []
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : int = 0, 1
while b <= n:
if b % 2 == 0:
even_fibs.append(_a)
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : List[str] = b, a + b
... | 25 |
import unittest
import numpy as np
import torch
from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class _UpperCamelCase ( unittest.TestCase ):
'''simpl... | 25 | 1 |
from collections import defaultdict
def __UpperCamelCase ( A , A ):
UpperCamelCase__ = first_str.lower().strip()
UpperCamelCase__ = second_str.lower().strip()
# Remove whitespace
UpperCamelCase__ = first_str.replace(... | 469 | import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
__magic_name__ =logging.get_logger(__name__)
__magic_name__ =... | 469 | 1 |
'''simple docstring'''
from __future__ import annotations
import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module
import numpy as np
from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_availa... | 467 |
'''simple docstring'''
import warnings
from typing import List
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available
class UpperCAmelCase ( _snake_case ... | 467 | 1 |
"""simple docstring"""
from typing import Dict
from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.testing_utils import (
TestCasePlus,
execute_subprocess_async,
get_torch_dist_unique_port,
require_torch_multi_gpu,
require_torch_neuro... | 668 | """simple docstring"""
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, ByTaTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...test_tokenization_common import ... | 668 | 1 |
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():
... | 73 |
'''simple docstring'''
import csv
import tweepy
# Twitter API credentials
__lowerCamelCase = ''''''
__lowerCamelCase = ''''''
__lowerCamelCase = ''''''
__lowerCamelCase = ''''''
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> None:
# ... | 288 | 0 |
import contextlib
import os
import sqlitea
import pytest
from datasets import Dataset, Features, Value
from datasets.io.sql import SqlDatasetReader, SqlDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases, require_sqlalchemy
def __SCREAMI... | 702 |
import absl # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import six # noqa: F401 # Here to have a ni... | 311 | 0 |
"""simple docstring"""
import re
def __A ( a_ :str) -> str:
if len(re.findall('''[ATCG]''' , a_)) != len(a_):
raise ValueError('''Invalid Strand''')
return dna.translate(dna.maketrans('''ATCG''' , '''TAGC'''))
if __name__ == "__main__"... | 52 |
'''simple docstring'''
a__ : Optional[Any] = '''Alexander Joslin'''
import operator as op
from .stack import Stack
def __lowerCamelCase ( UpperCAmelCase_ ) ->int:
snake_case__ = {'*': op.mul, '/': op.truediv, '+': op.add, '-': op.sub}
... | 368 | 0 |
"""simple docstring"""
from math import ceil
from typing import List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import BatchFeature, SequenceFeatureExtractor
from ...utils import TensorType, logging
l... | 549 |
"""simple docstring"""
def __lowerCAmelCase (_UpperCamelCase , _UpperCamelCase = False ):
if not isinstance(_UpperCamelCase , _UpperCamelCase ):
__lowerCAmelCase : str = F"Expected string as input, found {type(_UpperCamelCase )}"
raise ValueError(_UpperCamelCase )
if not i... | 549 | 1 |
"""simple docstring"""
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
A__ : str = datasets.logging.get_logger(__name__)
A__ : Dict = '\\n@InProceedings{m... | 153 |
"""simple docstring"""
import unittest
import numpy as np
import torch
from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device
from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_P... | 153 | 1 |
from __future__ import annotations
import typing
from collections import Counter
def snake_case__ ( __SCREAMING_SNAKE_CASE ) -> Union[str, Any]:
UpperCAmelCase_ = Counter()
for base in range(1 , max_perimeter + 1 ):
for perpendicular in range(_lowerCAmelCase , max_perim... | 700 |
import argparse
import torch
from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging
logging.set_verbosity_info()
def snake_case__ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) ... | 23 | 0 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class snake_case_ ( metaclass=lowerCamelCase_ ):
"""simple docstring"""
A_ = ['''torch''']
def __init__( self , *lowerCamelCase_ , **lowerCamelCase_) -> ... | 34 |
"""simple docstring"""
import unittest
import torch
from torch import nn
from accelerate.test_utils import require_cuda
from accelerate.utils.memory import find_executable_batch_size, release_memory
def __snake_case ( ):
"""simple docstring"""
raise RuntimeError('''CUDA out o... | 34 | 1 |
"""simple docstring"""
import argparse
import re
import requests
import torch
# git clone https://github.com/salesforce/BLIP.git
from models.blip import blip_decoder
from models.blip_itm import blip_itm
from models.blip_vqa import blip_vqa
from PIL import Image
from torchvision import transforms... | 721 |
"""simple docstring"""
import copy
from collections import OrderedDict
from typing import Dict, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
UpperCAmelCas... | 545 | 0 |
def _SCREAMING_SNAKE_CASE ( lowercase : int , lowercase : int ):
'''simple docstring'''
if a < 0 or b < 0:
raise ValueError('the value of both inputs must be positive' )
lowerCamelCase_ = str(bin(lowercase ) )[2:] # rem... | 70 |
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert import BertTokenizer
__lowercase = logging.get_logger(__name__)
__lowercas... | 203 | 0 |
'''simple docstring'''
def UpperCAmelCase ( lowercase__ : int = 600851475143 ):
'''simple docstring'''
try:
a__ = int(lowercase__ )
except (TypeError, ValueError):
raise TypeError("""Parameter n must be int or castable to int.""" )
if n <= 0:
... | 705 |
import argparse
import json
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 Ac... | 412 | 0 |
import numpy as np
from transformers import Pipeline
def lowerCAmelCase__(__snake_case ) -> Optional[Any]:
'''simple docstring'''
lowerCamelCase__ = np.max(__snake_case ,axis=-1 ,keepdims=__snake_case )
lowerCamelCase__ = np.exp(outputs - ... | 481 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_a = {
"configuration_mobilebert": [
"MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP",
"MobileBertConfig",... | 481 | 1 |
'''simple docstring'''
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
UNetaDConditionModel,
VideoToVideoSDPipeline,
)
from diffusers.utils import floats_ten... | 424 |
'''simple docstring'''
UpperCAmelCase_ : Union[str, Any] = [
"Audio",
"Array2D",
"Array3D",
"Array4D",
"Array5D",
"ClassLabel",
"Features",
"Sequence",
"Value",
"Image",
"Translation",
"TranslationVariableLanguages",
]
from .audio import Audio
from .feat... | 424 | 1 |
import itertools
import random
import unittest
import numpy as np
from transformers import BatchFeature, SpeechTaFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import SequenceFeatureE... | 613 | 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
a : Optional[int] = logging.get_logger(__name__)
a : Optional[Any] ... | 613 | 1 |
'''simple docstring'''
import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig
if TYPE_CHEC... | 701 |
'''simple docstring'''
from __future__ import annotations
def __a ( lowerCAmelCase__ : int , lowerCAmelCase__ : int ):
if b == 0:
return (1, 0)
((a__) , (a__)) : int = extended_euclid(lowerCAmelCase__ , a % b )
a__ : Opti... | 340 | 0 |
"""simple docstring"""
def UpperCamelCase ( _lowerCAmelCase : list, _lowerCAmelCase : list ) -> float:
_validate_point(_UpperCAmelCase )
_validate_point(_UpperCAmelCase )
if len(_UpperCAmelCase ) != len(_UpperCAmelCase ):
raise ValueError("""Both points... | 238 | '''simple docstring'''
import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
__UpperCamelCase: str = "M-CLIP"
def __init__( self : Union[str, Any] , A : ... | 244 | 0 |
import math
import sys
import cva
import numpy as np
def A__ ( _a : np.ndarray , _a : float ):
'''simple docstring'''
snake_case__ : Optional[Any] =math.sqrt(_a )
snake_case__ : Dict =1 / (sigma * math.sqrt(2 * math.pi ))
return ... | 448 |
import argparse
import torch
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt
if __name__ == "__main__":
__lowerCamelCase : List[str] = argparse.ArgumentParser()
parser.add_argument(
"""--checkpoint_path""", ... | 448 | 1 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Image
from .base import TaskTemplate
@dataclass(frozen=_a )
class __a( _a ):
"""simple docstring"""
lowerCAmelCase = field(default='''image-classifi... | 30 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
fr... | 299 | 0 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE... | 295 |
"""simple docstring"""
import collections
import os
import re
from pathlib import Path
lowercase_ : Union[str, Any] = '''src/transformers'''
# Matches is_xxx_available()
lowercase_ : str = re.compile(R'''is\_([a-z_]*)_available()''')
# Catches a one-line _import... | 295 | 1 |
import math
class __lowercase :
def UpperCamelCase__ ( self , A_ , A_ ) ->int:
'''simple docstring'''
__lowerCAmelCase : Any = 0.0
__lowerCAmelCase : List[str] = 0.0
for i in range(len(A_ ) ):
da += math.pow((sample[i... | 492 |
from .glue import glue_convert_examples_to_features, glue_output_modes, glue_processors, glue_tasks_num_labels
from .squad import SquadExample, SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
from .utils import DataProcessor, InputExample, InputFeatures, SingleSentenceClassificat... | 492 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__a = {
"""configuration_bigbird_pegasus""": [
"""BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""BigBirdPegasusConfig""",
"""BigBirdPegasusOnnxConfig""",... | 689 |
import os
import sys
import unittest
__a = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))
import check_dummies # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init ... | 689 | 1 |
"""simple docstring"""
from typing import Dict, Optional
import numpy as np
import datasets
A_ : Optional[int] ='\nIoU is the area of overlap between the predicted segmentation and the ground truth divided by the area of union\nbetween the predicted segmentation and the ground truth. For binary (tw... | 650 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowercase_: int = {
'configuration_lxmert': ['LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LxmertC... | 648 | 0 |
'''simple docstring'''
import inspect
import tempfile
from collections import OrderedDict, UserDict
from collections.abc import MutableMapping
from contextlib import ExitStack, contextmanager
from dataclasses import fields
from enum import Enum
from typing import Any, ContextManager, List, Tuple
import nu... | 703 |
'''simple docstring'''
def _lowerCamelCase ( lowercase : int = 100_0000 ) -> int:
_a = set(range(3 , lowercase , 2 ) )
primes.add(2 )
for p in range(3 , lowercase , 2 ):
if p not in primes:
continue... | 521 | 0 |
"""simple docstring"""
import random
class lowerCAmelCase_ :
'''simple docstring'''
@staticmethod
def _SCREAMING_SNAKE_CASE ( A_ : str ) -> tuple[list[int], list[int]]:
A = [ord(A_ ) for i in text]
A = []
A = []
for i in p... | 91 |
"""simple docstring"""
import os
import torch
from ..logging import get_logger
from .constants import FSDP_PYTORCH_VERSION, MODEL_NAME, OPTIMIZER_NAME
from .versions import is_torch_version
if is_torch_version('''>=''', FSDP_PYTORCH_VERSION):
import torch.distributed.checkpoint as dist_cp
from torch.distribu... | 91 | 1 |
# Author: OMKAR PATHAK, Nwachukwu Chidiebere
# Use a Python dictionary to construct the graph.
from __future__ import annotations
from pprint import pformat
from typing import Generic, TypeVar
SCREAMING_SNAKE_CASE__ : List[str] = TypeVar("""T""")
class lowerCamelCase_ ( Generic[T] ... | 180 |
import argparse
import math
import os
import torch
from neural_compressor.utils.pytorch import load
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, StableDiffusionPipeline, UNetaDConditionModel
def __lowercase ( ):
"""simple ... | 180 | 1 |
"""simple docstring"""
import argparse
import collections
import json
import os
import re
import string
import sys
import numpy as np
UpperCAmelCase = re.compile(r"""\b(a|an|the)\b""", re.UNICODE)
UpperCAmelCase = None
def _snake_case ( ):
"""simple docs... | 88 |
import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig
if TYPE_CHECKING:
... | 23 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_snake_case : List[str] = {"""configuration_van""": ["""VAN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """VanConfig"""]}
try:
if not is... | 493 |
'''simple docstring'''
import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset
from ..utils.generic import ModelOutput
class lowerCAmelCase ( __UpperCAmelCase ):
def __init__( self , UpperCamelCase , UpperCamelCase , UpperCamelCase ... | 493 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase = {'''configuration_mbart''': ['''MBART_PRETRAINED_... | 84 |
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase ... | 84 | 1 |
def UpperCamelCase__ ( UpperCAmelCase = 1000 ) -> int:
"""simple docstring"""
_a : Optional[int] = 2**power
_a : Union[str, Any] = 0
while n:
_a , _a : Optional[int] = r + n % 10, n // 10
re... | 307 |
import argparse
import torch
from transformers import LxmertConfig, LxmertForPreTraining, load_tf_weights_in_lxmert
from transformers.utils import logging
logging.set_verbosity_info()
def UpperCamelCase__ ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) -> Tuple:
... | 307 | 1 |
'''simple docstring'''
import argparse
from pathlib import Path
import torch
from transformers import OPTConfig, OPTModel
from transformers.utils import logging
logging.set_verbosity_info()
lowercase_ = logging.get_logger(__name__)
def lowerCAmelCase (__A):
"""simple docstring"""
... | 11 | """simple docstring"""
from __future__ import annotations
import time
A : List[str] = list[tuple[int, int]]
A : Tuple = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, ... | 516 | 0 |
'''simple docstring'''
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
UpperCamelCase : Dict = ''
UpperCamelCase : Any = ''
UpperCamelCase : Optional[Any] = ''
UpperCamelCase : Optional[Any] = 1 # (0 is vertica... | 9 |
'''simple docstring'''
def A__ ( ):
return [
a * b * (1000 - a - b)
for a in range(1 , 999 )
for b in range(__lowerCAmelCase , 999 )
if (a * a + b * b == (1000 - a - b) ** 2)
][0]
if __name__ == "__main__":
print(F... | 9 | 1 |
import math
def UpperCamelCase_( lowerCamelCase_ , lowerCamelCase_ ) -> float:
if initial_intensity < 0:
raise ValueError('The value of intensity cannot be negative' )
# handling of negative values of initial intensity
if angle < 0 or angle > 360:
rais... | 89 |
"""simple docstring"""
__snake_case : str = '\n# Installazione di Transformers\n! pip install transformers datasets\n# Per installare dalla fonte invece dell\'ultima versione rilasciata, commenta il comando sopra e\n# rimuovi la modalità commento al comando seguente.\n# ! pip... | 571 | 0 |
'''simple docstring'''
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import torch
from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
@dataclass
class __a( _a ):
"""simple docstri... | 710 |
import json
import os
import shutil
import tempfile
import unittest
from multiprocessing import get_context
from pathlib import Path
import datasets
import numpy as np
from datasets import load_dataset
from parameterized import parameterized
from transformers import AutoProcessor
from transformers.models.wavavec... | 300 | 0 |
'''simple docstring'''
def a__ ( _SCREAMING_SNAKE_CASE : int | float | str ) -> tuple[int, int]:
"""simple docstring"""
try:
UpperCAmelCase_ : int = float(_SCREAMING_SNAKE_CASE )
except ValueError:
raise ValueError("Please enter a valid numbe... | 71 |
import unittest
import numpy as np
import torch
from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class __a ( unitte... | 228 | 0 |
import argparse
import glob
import logging
import os
import time
from argparse import Namespace
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from torch.utils.data import DataLoader, TensorDataset
from transformers import glue_compute_metrics as comp... | 719 |
from ..utils import DummyObject, requires_backends
class A__ ( metaclass=__snake_case ):
_UpperCAmelCase :Tuple = ['note_seq']
def __init__( self , *A_ , **A_ ):
'''simple docstring'''
requires_backends(self , ["note_seq"] ... | 38 | 0 |
import sacrebleu as scb
from packaging import version
from sacrebleu import CHRF
import datasets
a__ = """\
@inproceedings{popovic-2015-chrf,
title = \"chr{F}: character n-gram {F}-score for automatic {MT} evaluation\",
author = \"Popovi{\'c}, Maja\",
booktitle = \"Proceedings of the Tenth Work... | 654 |
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class _lowerCAmelCase ( lowercase_ ):
"""simple do... | 654 | 1 |
import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional
import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto import tqdm
import transformers
from tr... | 704 |
"""simple docstring"""
import unittest
from .lib import (
Matrix,
Vector,
axpy,
square_zero_matrix,
unit_basis_vector,
zero_vector,
)
class SCREAMING_SNAKE_CASE_ ( unittest.TestCase ):
"""simple docstring"""
def __lowercase (... | 363 | 0 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
_UpperCamelCase = logging.get_logger(__name__)
_UpperCamelCase ... | 453 |
"""simple docstring"""
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 acce... | 453 | 1 |
"""simple docstring"""
from __future__ import annotations
from numpy import array, cos, cross, floataa, radians, sin
from numpy.typing import NDArray
def UpperCAmelCase ( A : float , A : float , A : bool = False ):
'''simple docstring'''
... | 715 |
"""simple docstring"""
import unittest
from transformers import (
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TextaTextGenerationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
fr... | 24 | 0 |
"""simple docstring"""
from typing import List, Union
import numpy as np
from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, logging
from .base import PIPELINE_INIT_ARGS, ArgumentHandler, ChunkPipeline
__UpperCAmelCase = logging.get_logger(__name__)
clas... | 642 |
"""simple docstring"""
from sklearn.metrics import matthews_corrcoef
import datasets
__UpperCAmelCase = '''
Compute the Matthews correlation coefficient (MCC)
The Matthews correlation coefficient is used in machine learning as a
measure of the quality of binary and multiclass classifications. It ... | 642 | 1 |
import json
import os
import tempfile
from transformers.testing_utils import check_json_file_has_correct_format
class UpperCamelCase__ :
'''simple docstring'''
__a : Tuple = None
def A__ ( self ) ->Any:
UpperCAmelCase__ :List[str] = ... | 433 |
from math import isqrt
def A ( SCREAMING_SNAKE_CASE ):
"""simple docstring"""
UpperCAmelCase__ :str = [True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j in range(i**2 , SCREAMING_SNAKE_CASE , SCREAMING_... | 433 | 1 |
import contextlib
import os
import sqlitea
import pytest
from datasets import Dataset, Features, Value
from datasets.io.sql import SqlDatasetReader, SqlDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases, require_sqlalchemy
def ... | 63 |
import copy
import fnmatch
import json
import os
import pickle as pkl
import shutil
import sys
import tarfile
import tempfile
from collections import OrderedDict
from contextlib import contextmanager
from functools import partial
from hashlib import shaaaa
from io import BytesIO
from pathlib... | 63 | 1 |
import asyncio
import os
import re
import sys
import tempfile
import unittest
from contextlib import contextmanager
from copy import deepcopy
from distutils.util import strtobool
from enum import Enum
from importlib.util import find_spec
from pathlib import Path
from unittest.mock import patch
import p... | 716 |
def __UpperCAmelCase ( snake_case_ : int = 6_0_0_8_5_1_4_7_5_1_4_3 ):
'''simple docstring'''
try:
UpperCAmelCase: Optional[int] = int(snake_case_ )
except (TypeError, ValueError):
raise TypeError("Parameter n must be int or castable to int." )
i... | 166 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_A : int = {"""configuration_glpn""": ["""GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GLPNConfig"""]}
try:
if not is_vision_available():
raise OptionalDepe... | 100 |
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.dummy_pt_objects impo... | 100 | 1 |
'''simple docstring'''
from __future__ import annotations
import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module
import numpy as np
from transformers import ViTMAEConfig
from transformers.file_utils import cach... | 98 |
'''simple docstring'''
import contextlib
import csv
import json
import os
import sqlitea
import tarfile
import textwrap
import zipfile
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
import datasets
import datasets.config
@pytest.fixture(scope='''session''' )
def... | 98 | 1 |
'''simple docstring'''
import ast
import os
import re
import shutil
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.test_utils.examples import compare_against_test
from accelerate.test_utils.testing import TempDirTestCase, require_trackers, run_comm... | 90 |
'''simple docstring'''
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, _concaten... | 229 | 0 |
'''simple docstring'''
from ...processing_utils import ProcessorMixin
class _snake_case ( a_ ):
SCREAMING_SNAKE_CASE : Union[str, Any] = ['''image_processor''', '''feature_extractor''']
SCREAMING_SNAKE_CASE : Optional[int] = '''TvltImageProcessor'''
SCREAMING_SNAKE... | 514 |
'''simple docstring'''
import torch
def snake_case ( ) -> List[str]:
"""simple docstring"""
if torch.cuda.is_available():
lowerCAmelCase = torch.cuda.device_count()
else:
lowerCAmelCase = 0
print(F'Successfully ran on {num_gpus} GPUs' )
if __... | 514 | 1 |
import argparse
import importlib
from pathlib import Path
# Test all the extensions added in the setup
a : Tuple = [
'kernels/rwkv/wkv_cuda.cu',
'kernels/rwkv/wkv_op.cpp',
'kernels/deformable_detr/ms_deform_attn.h',
'kernels/deformable_detr/cuda/ms_deform_im2col_cuda.c... | 556 |
"""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 impo... | 510 | 0 |
from __future__ import annotations
import unittest
from transformers import MobileBertConfig, is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import T... | 138 | from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE : Optional[Any] = {
"configuration_clap": [
"CLAP_PRETRAINED_MODEL_ARCHIVE_LIST",
"ClapAudioConfig",
"ClapConfig",
"Cl... | 138 | 1 |
import argparse
import os
import torch
from transformers import FlavaImageCodebook, FlavaImageCodebookConfig
def A ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ) -> Tuple:
A__ = s.rsplit(__UpperCamelCase , __UpperCamelCase ... | 9 |
from __future__ import annotations
from fractions import Fraction
def A ( __UpperCamelCase , __UpperCamelCase ) -> bool:
return (
num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num / den
)
def A ( __UpperCamelCase ) -> list[str]:... | 9 | 1 |
from __future__ import annotations
def __UpperCamelCase ( A , A ):
UpperCamelCase__ = get_failure_array(A )
# 2) Step through text searching for pattern
UpperCamelCase__ , UpperCamelCase__ = 0, 0 # index into text, pat... | 469 | def __UpperCamelCase ( A = 600851475143 ):
try:
UpperCamelCase__ = int(A )
except (TypeError, ValueError):
raise TypeError('''Parameter n must be int or castable to int.''' )
if n <= 0:
raise ValueError('''Parameter n must be ... | 469 | 1 |
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class UpperCAmelCase__ ( snake_case ):
"""simple docstring"""
@staticmethod
@abstractmethod
def _UpperCAmelCase ( __lowerCAmelCase: ArgumentParser ) -> Optional[Any]:
'''simple docs... | 221 | def __lowerCAmelCase ( A_ : str ) -> str:
__UpperCAmelCase = ""
for ch in key:
if ch == " " or ch not in key_no_dups and ch.isalpha():
key_no_dups += ch
return key_no_dups
def __lowerCAmelCase ( A_ : str ) -> dict[str, str]:
__UpperCAmelCase ... | 221 | 1 |
from __future__ import annotations
def lowerCamelCase_ ( A : float , A : float , A : float , ):
"""simple docstring"""
if (electron_conc, hole_conc, intrinsic_conc).count(0 ) != 1:
raise ValueError('''You cannot supply more or less than 2 values''' )
el... | 413 |
import os
import shutil
from pathlib import Path
from typing import Optional, Union
import numpy as np
from huggingface_hub import hf_hub_download
from ..utils import ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME, is_onnx_available, logging
if is_onnx_available():
import onnxruntime as ort
_snak... | 413 | 1 |
import multiprocessing
from typing import TYPE_CHECKING, Optional, Union
from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream
if TYPE_CHECKING:
import sqlitea
im... | 233 |
'''simple docstring'''
import shutil
import tempfile
import unittest
from unittest.mock import patch
from transformers import (
DefaultFlowCallback,
IntervalStrategy,
PrinterCallback,
ProgressCallback,
Trainer,
TrainerCallback,
TrainingArguments,
is_torch_available,
)
from t... | 38 | 0 |
"""simple docstring"""
def _snake_case ( UpperCAmelCase_ : int = 100_0000 ):
A__ = [i - 1 for i in range(limit + 1 )]
for i in range(2 , limit + 1 ):
if phi[i] == i - 1:
for j in range(2 * i , limit + 1 , UpperCAmelCase_ ... | 702 |
"""simple docstring"""
import math
import sys
def _snake_case ( UpperCAmelCase_ : int ):
if number != int(UpperCAmelCase_ ):
raise ValueError("""the value of input must be a natural number""" )
if number < 0:
raise ValueError("""the value of in... | 500 | 0 |
import re
def _UpperCamelCase (a__ :str ):
"""simple docstring"""
UpperCamelCase__ = re.compile(r"""^(\+91[\-\s]?)?[0]?(91)?[789]\d{9}$""" )
if match := re.search(a__ , a__ ):
return match.string == phone
return Fals... | 619 |
import unittest
from huggingface_hub import hf_hub_download
from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VideoMAEFeatureExtractor
from transformers.pipelines import VideoClassificationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
r... | 619 | 1 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.activations import gelu_new, gelu_python, get_activation
@require_torch
class lowercase__ ( unittest.TestCase... | 705 |
import logging
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import arg_to_scheduler
from transformers import TrainingArguments
A = logging.getLogger(__name__)
@dataclass
class lowercase__ ( __SCREAMING_SNAKE_CASE ):
A__= field(
... | 277 | 0 |
"""simple docstring"""
from typing import List, Optional, Union
import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, Tens... | 7 |
'''simple docstring'''
def _UpperCamelCase (_lowerCamelCase : Optional[Any] )-> Dict:
'''simple docstring'''
__snake_case = []
__snake_case = []
__snake_case = {
'''^''': 3,
'''*''': 2,
'''/''': 2,
... | 24 | 0 |
def __SCREAMING_SNAKE_CASE ( lowerCAmelCase: str , lowerCAmelCase: str ) -> int:
if len(lowerCAmelCase ) != len(lowerCAmelCase ):
raise ValueError("String lengths must match!" )
_UpperCAmelCase : List[Any] = 0
for chara, chara in zip(lowerCAmelCase , ... | 467 |
import logging
import os
from .state import PartialState
class a ( logging.LoggerAdapter ):
@staticmethod
def _UpperCAmelCase ( A_ ):
'''simple docstring'''
_UpperCAmelCase : Tuple = PartialState()
return not main_process_only ... | 467 | 1 |
"""simple docstring"""
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... | 575 |
"""simple docstring"""
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from a... | 575 | 1 |
'''simple docstring'''
import json
import os
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowercase : Optional[Any] = logging.get_logger(__name__)
lowercase : Optional[int] = {
'vocab_fil... | 159 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Callable
from typing import Any, Generic, TypeVar
lowercase : Union[str, Any] = TypeVar('T')
class _lowerCAmelCase ( Generic[T] ):
"""simple docstring"""
def __init__( self ... | 159 | 1 |
import json
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_i... | 201 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__lowerCAmelCase = {
'configuration_bridgetower': [
'BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'BridgeTowerConfig',
'BridgeTowerTextCo... | 201 | 1 |
import copy
import os
from typing import TYPE_CHECKING, List, Union
if TYPE_CHECKING:
pass
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case_ : Any = logging.get_logger(__name__)
snake_case_ : Union[str, Any] = {
'ka... | 718 |
from __future__ import annotations
import copy
import tempfile
import unittest
from transformers import CONFIG_MAPPING, AutoConfig, BertConfig, GPTaConfig, TaConfig, TapasConfig, is_tf_available
from transformers.testing_utils import (
DUMMY_UNKNOWN_IDENTIFIER,
SMALL_MODEL_IDENTIFIER,
RequestC... | 166 | 0 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import load_numpy, slow... | 17 |
'''simple docstring'''
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2... | 98 | 0 |
'''simple docstring'''
def A__ ( _a : int , _a : int ):
'''simple docstring'''
if number < 0 or shift_amount < 0:
raise ValueError("""both inputs must be positive integers""" )
snake_case__ : List[Any] =str(bin(__A ) )
binary_number += "0" * shif... | 708 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import (
DiffusionPipeline,
UnCLIP... | 448 | 0 |
"""simple docstring"""
import logging
import math
import os
from dataclasses import dataclass, field
from glob import glob
from typing import Optional
from torch.utils.data import ConcatDataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_WITH_LM_HEAD_MAPPING,
AutoConfig,
A... | 506 |
"""simple docstring"""
import logging
import math
import os
from dataclasses import dataclass, field
from glob import glob
from typing import Optional
from torch.utils.data import ConcatDataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_WITH_LM_HEAD_MAPPING,
AutoConfig,
... | 222 | 0 |
from __future__ import annotations
def _lowerCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
'''simple docstring'''
A_ = []
create_all_state(1 , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , [] , SCREAMING_SNAKE... | 700 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ....tokenization_utils_fast import PreTrainedTokenizerFast
from ....utils import logging
from .tokenization_retribert import RetriBertTokenizer
__lowercase = logging.get_logger(__name__)
__lowercase = {... | 563 | 0 |
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
A_: List[Any] = logging.get_logger(__name__)
def __lowerCAmelCase ( _A ):
"""simple docstring"""
_lowercase = ... | 398 |
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class lowerCamelCase ( __lowerCamelCase , __lowerCamelCase ):
@register_to_config
def __init__( self :Tuple , *,
lowercase :i... | 201 | 0 |
import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type
from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterators import ThreadedIterator
from tqdm imp... | 60 |
from __future__ import annotations
from random import random
from typing import Generic, TypeVar
__snake_case :Any = TypeVar('''KT''')
__snake_case :List[str] = TypeVar('''VT''')
class _A ( Generic[KT, VT] ):
def __init__( self : Dict , __SCREAMING_SNAKE_CASE : KT | ... | 60 | 1 |
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,
... | 61 |
# 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 app... | 454 | 0 |
def _lowercase ( lowercase__ ):
__lowerCAmelCase : str = len(lowercase__ )
for i in range(1 , lowercase__ ):
__lowerCAmelCase : Optional[Any] = collection[i]
__lowerCAmelCase : Any = 0
__lowerCAmelCase :... | 583 |
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable
_UpperCamelCase = {
"configuration_gpt_neox_japanese": ["GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoXJapaneseConfig"],
"tokenization_gpt_neox_j... | 583 | 1 |
lowerCAmelCase = [0, 2, 4, 6, 8]
lowerCAmelCase = [1, 3, 5, 7, 9]
def _a ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
"""simple docstring"""
if remaining_length == 0:
if digits[0] == 0 or digits[-1] ... | 43 |
def snake_case_ ( SCREAMING_SNAKE_CASE_ ) -> int:
if not isinstance(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ):
raise TypeError("Input value must be an 'int' type" )
lowercase__ : str = 0
while number:
... | 397 | 0 |
'''simple docstring'''
import json
import re
from typing import TYPE_CHECKING, List, Optional, Tuple, Union
import numpy as np
from ...utils import is_tf_available, is_torch_available, logging
if TYPE_CHECKING:
if is_torch_available():
import torch
if is_tf_available... | 159 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaControlnetImgaImgPipeline,
KandinskyVaaPriorEmbaEmbPipeline,
UNetaDConditionModel,
VQModel,
)
from diffuse... | 159 | 1 |
'''simple docstring'''
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
execute_subprocess_async,
require_cpu,
require_huggingface_suite,
require_multi_gpu,
require_single_g... | 539 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase_ = {
"configuration_deberta": ["DEBERTA_PRETRAINED_CONFIG... | 539 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase = logging.get_logger(__name__)
lowercase = {
"google/pegasus-large": "https://huggingface.co/google/pegasus-large/resolve/main/config.json",
# See all PEGASUS models at https://h... | 718 | # 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 TensorFormatter
if TYP... | 591 | 0 |
import math
class lowerCAmelCase_ :
def __init__( self : Tuple , _A : int=0 ): # a graph with Node 0,1,...,N-1
_UpperCamelCase = n
_UpperCamelCase = [
[math.inf for j in range(0 , _A )] for i in range(0 , _A... | 10 |
import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import logging
f... | 278 | 0 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCamelCase : Dict = logging.get_logger(__name__)
__lowerCamelCase : Tuple = {
'''microsoft/unispeech-sat-base-100h-libri-ft''': (
'''https://h... | 316 | import os
from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE, hf_cache_home
__lowerCamelCase : Dict = HUGGINGFACE_HUB_CACHE
__lowerCamelCase : Union[str, Any] = '''config.json'''
__lowerCamelCase : Tuple = '''diffusion_pytorch_model.bin'''
__l... | 316 | 1 |
import pickle
import unittest
import torch
from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu
@require_cpu
class _snake_case ( unittest.TestCase ):
def SCREAMING_SNAKE_CASE__ ( self ):
a ... | 445 | '''simple docstring'''
import logging
import re
import pytorch_quantization
import pytorch_quantization.nn as quant_nn
import torch
from pytorch_quantization import calib
from pytorch_quantization.tensor_quant import QuantDescriptor
UpperCamelCase__ : Optional[Any] = logging.getLogger(__name__)
Uppe... | 614 | 0 |
from __future__ import annotations
from collections.abc import MutableSequence
class __magic_name__ :
"""simple docstring"""
def __init__( self , a__ , a__ ):
if len(a__ ) != degree + 1:
raise ValueError(
'''The number of coefficients should be equal to the degree + 1.... | 297 |
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_bird.modeling_fla... | 297 | 1 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_availabl... | 19 |
'''simple docstring'''
from collections import Counter
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
snake_case_ = datasets.load_iris()
snake_case_ = np.array(data["""data"""])
snake_case_ = np.array(data["""target"""])
snake_case_ = ... | 507 | 0 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE_:Tuple = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_:Optional[int] = {
"""BAAI/AltCLIP""": """https://huggingface.co/BAAI/AltCLIP/resolve/main/c... | 718 |
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,
)
fr... | 520 | 0 |
"""simple docstring"""
def A_ ( snake_case_ : int ,snake_case_ : int ):
'''simple docstring'''
return int(input_a == input_a == 0 )
def A_ ( ):
'''simple docstring'''
print("""Truth Table of NOR Gate:""" )
print("""| Inp... | 499 |
"""simple docstring"""
def A_ ( snake_case_ : int = 1_0_0_0_0_0_0 ):
'''simple docstring'''
UpperCamelCase : List[Any] = [i - 1 for i in range(limit + 1 )]
for i in range(2 ,limit + 1 ):
if phi[i] == i - 1:
for j in range(... | 499 | 1 |
"""simple docstring"""
import math
from typing import Any, Callable, List, Optional, Tuple, Union
import numpy as np
import torch
from ...models import TaFilmDecoder
from ...schedulers import DDPMScheduler
from ...utils import is_onnx_available, logging, randn_tensor
if is_onnx_available():
from .... | 165 |
"""simple docstring"""
def _lowerCAmelCase(a : int ) -> bool:
if p < 2:
raise ValueError('''p should not be less than 2!''' )
elif p == 2:
return True
_SCREAMING_SNAKE_CASE =4
_SCREAMING_SNAKE_CASE =(1 << p) - 1
for _ in range(p - 2 ):
_SCREAMING_S... | 165 | 1 |
import numpy as np
def UpperCamelCase ( __lowerCamelCase : np.ndarray ):
return 1 / (1 + np.exp(-vector ))
def UpperCamelCase ( __lowerCamelCase : np.ndarray ):
return vector * sigmoid(__lowerCamelCase )
if __name__ == "__main__"... | 204 |
import os
from argparse import ArgumentParser, Namespace
from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand
if not is_tf_available... | 204 | 1 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusion... | 712 |
"""simple docstring"""
from dataclasses import dataclass
from typing import Dict, Optional, Union
import torch
import torch.nn.functional as F
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .at... | 228 | 0 |
import operator
def A ( _lowerCamelCase , _lowerCamelCase = False , _lowerCamelCase = None ):
'''simple docstring'''
_lowerCAmelCase : Union[str, Any] = operator.lt if reverse else operator.gt
_lowerCAmelCase : int = ... | 500 |
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging
_snake_case = loggi... | 500 | 1 |
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
from ...test_modeling_common import Mo... | 138 | import argparse
from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging
logging.set_verbosity_info()
def lowerCAmelCase_ ( _SCREAMING_SNAKE_CASE : List[str] , _SCREAMING_SNAKE_CASE : Any , _SCREA... | 138 | 1 |
import unittest
from transformers.testing_utils import require_bsa
from transformers.utils import is_bsa_available
from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin
if is_bsa_available():
from transformers import MarkupLMFeatureExtractor
class __magic_na... | 628 |
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
def lowerCA... | 628 | 1 |
import numpy as np
def A__ ( __lowerCamelCase ):
return 1 / (1 + np.exp(-vector ))
def A__ ( __lowerCamelCase ):
return vector * sigmoid(1.7_02 * vector )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 597 |
def A__ ( __lowerCamelCase, __lowerCamelCase ):
SCREAMING_SNAKE_CASE_ = len(__lowerCamelCase )
SCREAMING_SNAKE_CASE_ = [[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 element
# hence True/1
for i in ra... | 597 | 1 |
"""simple docstring"""
from io import BytesIO
from typing import List, Union
import requests
from ..utils import add_end_docstrings, is_decord_available, is_torch_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_decord_available():
import numpy as np
from... | 589 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepi... | 351 | 0 |
from __future__ import annotations
def __UpperCamelCase ( a, a = None) ->List[str]:
lowerCamelCase__ = word_bank or []
# create a table
lowerCamelCase__ = len(snake_case_) + 1
lowerCamelCase__ = []
for _ in range(snake_case_):
... | 710 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging
if TYPE_CHECKING:
from ...onnx.config import PatchingSpec
from ...tok... | 360 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ : Union[str, Any] = logging.get_logger(__name__)
lowercase__ : Dict = {
'facebook/vit-mae-base': 'https://huggingface.co/facebook/vit-mae-base/resolve/main/config.j... | 98 |
'''simple docstring'''
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... | 314 | 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
)
_snake_case = logging.getLogger(__name__... | 715 |
"""simple docstring"""
import unittest
from transformers import MPNetConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attenti... | 491 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCamelCase : Any = {
'''configuration_blip_2''': [
'''BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''Blip2Config''',
... | 4 |
import math
def UpperCamelCase ( _A, _A ):
"""simple docstring"""
__magic_name__ : Optional[int] = len(_A )
__magic_name__ : Tuple = int(math.floor(math.sqrt(_A ) ) )
__magic_name__ : Optional[int] = 0
w... | 324 | 0 |
import re
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class UpperCAmelCase__ ( lowercase__ ):
"""simple docstring"""
__UpperCAmelCase : Any = ['''image_processor''', '''tokenizer''']
__UpperCAmelCase : ... | 715 |
'''simple docstring'''
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
import math
from typing import Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerM... | 319 | 0 |
'''simple docstring'''
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union
# Build the list of all image processors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trus... | 41 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
... | 483 | 0 |
"""simple docstring"""
from __future__ import annotations
from pprint import pformat
from typing import Generic, TypeVar
__A = TypeVar('''T''')
class _snake_case ( Generic[T] ):
def __init__( self : Union[str, Any] , UpperCAmelCase : Any ... | 719 | """simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_ava... | 366 | 0 |
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