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 |
|---|---|---|---|---|
from typing import Dict, List, Optional, Tuple, Union
import torch
from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class snake_cas... | 625 | """simple docstring"""
def snake_case__ ( _snake_case : int ):
"""simple docstring"""
if number > 0:
raise ValueError("input must be a negative integer" )
UpperCamelCase__ = len(bin(_snake_case )[3:] )
UpperCamelCase__ ... | 516 | 0 |
from typing import Optional, Union
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin
class A_ ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
@register_to_config
d... | 706 |
a ="""0.18.2"""
from .configuration_utils import ConfigMixin
from .utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_inflect_available,
is_invisible_watermark_available,
is_k_diffusion_available,
is_k_diffusion_version,
is_librosa_available,
is_note_seq... | 337 | 0 |
"""simple docstring"""
import argparse
import os
import torch
from transformers import (
XLNetConfig,
XLNetForQuestionAnswering,
XLNetForSequenceClassification,
XLNetLMHeadModel,
load_tf_weights_in_xlnet,
)
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, ... | 293 |
import math
import os
import sys
def lowerCamelCase__ (_UpperCAmelCase):
SCREAMING_SNAKE_CASE = ''
try:
with open(_UpperCAmelCase , 'rb') as binary_file:
SCREAMING_SNAKE_CASE = binary_file.read()
for dat in data:
SCREAMING_SNAKE_CASE ... | 73 | 0 |
"""simple docstring"""
import unittest
from knapsack import greedy_knapsack as kp
class _lowercase ( unittest.TestCase ):
"""simple docstring"""
def UpperCAmelCase_ ( self : Dict ) -> List[Any]:
'''simple docstr... | 296 | """simple docstring"""
from __future__ import annotations
import unittest
from transformers import LEDConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
... | 296 | 1 |
import sys
def __magic_name__ ( __lowerCAmelCase : str ) -> Union[str, Any]:
__lowerCamelCase = len(__lowerCAmelCase )
__lowerCamelCase = [[0 for x in range(__lowerCAmelCase )] for x in range(__lowerCAmelCase )]
__lowerCamelCase = ... | 298 |
def __magic_name__ ( __lowerCAmelCase : Any , __lowerCAmelCase : Optional[int] ) -> Optional[Any]:
__lowerCamelCase = [1]
for i in range(2 , __lowerCAmelCase ):
factorials.append(factorials[-1] * i )
assert 0 <= k < factorials[-1] * ... | 298 | 1 |
'''simple docstring'''
import argparse
import glob
import importlib.util
import os
import re
import black
from doc_builder.style_doc import style_docstrings_in_code
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
UpperCAmelCa... | 711 |
'''simple docstring'''
import argparse
import shutil
import time
from json import JSONDecodeError
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoToken... | 540 | 0 |
import collections
import inspect
import unittest
from typing import Dict, List, Tuple
from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_available
from ...test_backbone_common import B... | 655 |
from __future__ import annotations
class lowerCAmelCase :
def __init__( self :Union[str, Any] , _lowercase :List[Any]=None ):
'''simple docstring'''
lowercase__ = data
lowercase__ = None
def __repr__( self :Dict ... | 655 | 1 |
from maths.prime_factors import prime_factors
def A ( _UpperCAmelCase : int ) -> int:
'''simple docstring'''
if not isinstance(_UpperCAmelCase , _UpperCAmelCase ):
_UpperCAmelCase = F"Input value of [number={number}] must be an integer"
... | 710 |
from collections import Counter
from timeit import timeit
def A ( _UpperCAmelCase : str = "" , ) -> bool:
'''simple docstring'''
return sum(c % 2 for c in Counter(input_str.replace(' ' , '' ).lower() ).values() ) < 2
def A ... | 639 | 0 |
import datasets
from .evaluate import evaluate
_lowerCAmelCase : Tuple ='''\
@article{hendrycks2021cuad,
title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},
author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},
journal={arXiv preprint arXiv:2103.062... | 113 |
"""simple docstring"""
from jiwer import compute_measures
import datasets
__lowerCAmelCase : Tuple = '''\
@inproceedings{inproceedings,
author = {Morris, Andrew and Maier, Viktoria and Green, Phil},
year = {2004},
month = {01},
pages = {},
title ... | 58 | 0 |
'''simple docstring'''
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class _snake_case ( __lowercase ):
_lowerc... | 705 |
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForSequenceClassification,
... | 444 | 0 |
'''simple docstring'''
from timeit import timeit
a_ : List[Any] = {
"""MALAYALAM""": True,
"""String""": False,
"""rotor""": True,
"""level""": True,
"""A""": True,
"""BB""": True,
"""ABC""": False,
"""amanaplanacanalpanama""": True, # "a man a p... | 675 |
'''simple docstring'''
from arguments import InitializationArguments
from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser
# Configuration
a_ : Optional[int] = HfArgumentParser(InitializationArguments)
a_ : str = parser.pa... | 675 | 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 rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
ImageInput,
PILImageResampl... | 712 |
"""simple docstring"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional
from packaging import version
if TYPE_CHECKING:
from ... import PreTrainedTokenizer, TensorType
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig... | 299 | 0 |
"""simple docstring"""
import unittest
from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
@re... | 289 |
import argparse
import os
import gluonnlp as nlp
import mxnet as mx
import numpy as np
import torch
from gluonnlp.base import get_home_dir
from gluonnlp.model.bert import BERTEncoder
from gluonnlp.model.utils import _load_vocab
from gluonnlp.vocab import Vocab
from packaging import version
from t... | 615 | 0 |
"""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 (
MobileViTConfig,
MobileViTForImageClassification,
MobileViTForSemanticSegmentation,
MobileViTImageProce... | 713 |
"""simple docstring"""
from argparse import ArgumentParser, Namespace
from ..utils import logging
from . import BaseTransformersCLICommand
def a__ ( a : Namespace ):
"""simple docstring"""
return ConvertCommand(
args.model_type , args.tf_checkpoint , args.pytorch_du... | 87 | 0 |
'''simple docstring'''
import warnings
from pathlib import Path
from typing import List, Tuple, Union
import fire
from torch import nn
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer, PreTrainedModel
from transformers.utils import logging
lowerCAmelCase__ = ... | 596 |
'''simple docstring'''
import os
import re
import warnings
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
... | 596 | 1 |
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pytest
from datasets.arrow_dataset import Dataset
from datasets.search import ElasticSearchIndex, FaissIndex, MissingIndex
from .utils import require_elasticsearch, requi... | 657 |
from math import pi
def __lowercase ( __lowerCAmelCase : int , __lowerCAmelCase : int ):
return 2 * pi * radius * (angle / 3_6_0)
if __name__ == "__main__":
print(arc_length(90, 10))
| 657 | 1 |
"""simple docstring"""
from __future__ import annotations
def a__ ( SCREAMING_SNAKE_CASE : list[int] , SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
lowerCAmelCase : list[list[int]] = []
lowerCAmelCase : list[int] = []
... | 645 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE : int = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE : Union[str, Any] = {
"google/switch-base-8": "https://huggingface.co/google/switch-base-8/blob/main/config.json",
}
... | 419 | 0 |
'''simple docstring'''
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextModel,
CLIPTokenizer,
WhisperForConditionalGeneration,
WhisperProcessor,
)
from diffusers import (
AutoencoderKL,
... | 704 |
'''simple docstring'''
def lowercase_ ( _lowercase = 1_000 ) -> int:
'''simple docstring'''
lowerCamelCase_ : Any = -1
lowerCamelCase_ : Optional[Any] = 0
for a in range(1 , n // 3 ):
# Solving the two equations a**2+b**2=c**2 and a... | 357 | 0 |
class __A :
def __init__( self :List[str] , __snake_case :str , __snake_case :Optional[Any] ):
'''simple docstring'''
__magic_name__ : int =name
__magic_name__ : Optional[int] =val
def __str__( self ... | 21 |
from typing import List
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase : Any = logging.get_logger(__name__)
_lowercase : Union[str, Any] = {
"snap-research/efficientformer-l1-300": (
"https://huggingface.co/snap-research... | 641 | 0 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_dif... | 717 |
'''simple docstring'''
import pytest
from datasets.parallel import ParallelBackendConfig, parallel_backend
from datasets.utils.py_utils import map_nested
from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows
def __UpperCAmelCase ( _UpperCAmelCase : Dict ... | 680 | 0 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE_ ( __A : list[int] , __A : list[int] , __A : int ) -> bool:
return not any(
neighbour == 1 and colored_vertices[i] == color
for i, neighbour in enumerate(__A ) )
def SCREAMING_SNA... | 418 |
'''simple docstring'''
import json
import os
import unittest
from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cach... | 418 | 1 |
import json
import os
import unittest
from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property
from ...test_tok... | 1 |
import argparse
import os
import re
import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor
from accelerate import Acceler... | 1 | 1 |
'''simple docstring'''
from itertools import product
def __SCREAMING_SNAKE_CASE ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
_snake_case = sides_number
_snake_case = max_face_number * dice_number
_snake_case = [0] * (max_total + 1)
_snake_case = 1
... | 585 |
import argparse
import shlex
import runhouse as rh
if __name__ == "__main__":
# Refer to https://runhouse-docs.readthedocs-hosted.com/en/latest/api/python/cluster.html#hardware-setup for cloud access
# setup instructions, if using on-demand hardware
# If user passes --user <user> --host <host> --key_... | 282 | 0 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
A = logging.get_logger(__name__)
A = {'vocab_file... | 449 |
'''simple docstring'''
# Imports
import numpy as np
class __snake_case :
def __init__( self, A=None, A=None, A=None, A=None, A=None ):
"""simple docstring"""
self.set_matricies(red=A, green=A, blue=A, red_edge=A, nir=A ... | 449 | 1 |
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 ... | 108 |
'''simple docstring'''
def lowerCamelCase ( _snake_case : int = 50_000_000 ):
'''simple docstring'''
lowercase__ = set()
lowercase__ = int((limit - 24) ** (1 / 2) )
lowercase__ = set(range(3 ,prime_square_... | 267 | 0 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenizati... | 715 |
'''simple docstring'''
def _SCREAMING_SNAKE_CASE( snake_case_ : float ) ->float:
'''simple docstring'''
if edge <= 0 or not isinstance(snake_case_ , snake_case_ ):
raise ValueError('''Length must be a positive.''' )
... | 411 | 0 |
'''simple docstring'''
import os
import unittest
from transformers.models.cpmant.tokenization_cpmant import VOCAB_FILES_NAMES, CpmAntTokenizer
from transformers.testing_utils import require_jieba, tooslow
from ...test_tokenization_common import TokenizerTesterMixin
@require_jieba
class __lowerCAmelCase ... | 452 |
'''simple docstring'''
import unittest
from transformers import AutoConfig, AutoTokenizer, BertConfig, TensorType, is_flax_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, slow
if is_flax_available():
import jax
from transformers.models.auto.modeling_flax_aut... | 452 | 1 |
"""simple docstring"""
import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class ... | 703 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase_ : Tuple = logging.get_logger(__name__)
lowerCAmelCase_ : List[str]... | 378 | 0 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer
_UpperCAmelCase : str = logging.get_logger(_... | 683 |
'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging
if TYPE_CHECKING:
from ...feature_extraction_utils i... | 683 | 1 |
'''simple docstring'''
# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
# python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git w... | 7 |
'''simple docstring'''
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class __magi... | 7 | 1 |
'''simple docstring'''
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import SegformerImageProcessor, SwinConfig, UperNetConfig, UperNetForSemanticSegmentation
def _lowercase ( lowerCamelCase__ ... | 168 |
from __future__ import annotations
from math import pi
from typing import Protocol
import matplotlib.pyplot as plt
import numpy as np
class __lowercase ( UpperCAmelCase_ ):
"""simple docstring"""
def _SCREAMING_SNAKE_CASE ( self : List[Any] , lowerCAmelCase__ ... | 671 | 0 |
"""simple docstring"""
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 ... | 109 |
"""simple docstring"""
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import (
... | 109 | 1 |
import argparse
import json
from tqdm import tqdm
def __snake_case ( ):
"""simple docstring"""
A_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--src_path" ,type=__UpperCamelCase ,default="biencoder-nq-dev.j... | 86 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_torch_available,
)
a = {
"configuration_speecht5": [
"SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP",
"SPEECHT5_PRETRAINE... | 518 | 0 |
"""simple docstring"""
import os
from distutils.util import strtobool
def UpperCAmelCase__ (lowerCAmelCase_ , lowerCAmelCase_ ):
'''simple docstring'''
for e in env_keys:
__SCREAMING_SNAKE_CASE = int(os.environ.get(lowerCAmelCase_ , -1 ) )... | 553 |
"""simple docstring"""
from __future__ import annotations
def UpperCAmelCase__ (lowerCAmelCase_ ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = str(lowerCAmelCase_ )
return n == n[::-1]
def UpperCAmelCase__ (lowerCAmelCase_ = 100_0000 ):
... | 553 | 1 |
'''simple docstring'''
import tempfile
import torch
from diffusers import IPNDMScheduler
from .test_schedulers import SchedulerCommonTest
class SCREAMING_SNAKE_CASE ( _a ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = (IPNDMSchedule... | 430 |
'''simple docstring'''
import argparse
import os
import re
_lowerCamelCase : int = "src/transformers/models/auto"
# re pattern that matches mapping introductions:
# SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
_lowerCamelCase : Union... | 430 | 1 |
'''simple docstring'''
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotA... | 319 |
'''simple docstring'''
import json
import os
from typing import Optional
import numpy as np
from ...feature_extraction_utils import BatchFeature
from ...processing_utils import ProcessorMixin
from ...utils import logging
from ...utils.hub import get_file_from_repo
from ..auto import AutoTokenizer
__lowerCAm... | 319 | 1 |
"""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 timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import ... | 52 |
"""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 t... | 571 | 0 |
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.model... | 711 | import logging
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import arg_to_scheduler
from transformers import TrainingArguments
_lowercase : Any =logging.getLogger(__name__)
@dataclass
class SCREAMING_SNAKE_CASE_ ( lowerCAmelCa... | 661 | 0 |
'''simple docstring'''
import re
from filelock import FileLock
try:
import nltk
A_ : Optional[int] = True
except (ImportError, ModuleNotFoundError):
A_ : List[Any] = False
if NLTK_AVAILABLE:
with FileLock(".lock") as lock:
nltk.download("punkt", quiet=True)
... | 38 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase : List[str] = logging.get_logger(__name__)
UpperCAmelCase : Any = {
'''google/fnet-base''': '''https://huggingface.co/google/fnet-base/resolve/main/config.json''',
'''google/fnet-large'''... | 239 | 0 |
class UpperCamelCase__ :
def __init__( self : Optional[int] , UpperCamelCase__ : List[Any] , UpperCamelCase__ : Dict , UpperCamelCase__ : int ):
'''simple docstring'''
lowercase_ = name
... | 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 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
SCREAMING_SNAKE_CASE__ : Tuple = {
"""configuration_mobilevit""": ["""MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", ... | 0 |
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__ ... | 183 | 0 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
A_ : Dict = {
"configuration_efficientnet": [
"EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP",
... | 703 |
"""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_availa... | 616 | 0 |
from __future__ import annotations
import math
def _A( UpperCamelCase__ : int , UpperCamelCase__ : int , UpperCamelCase__ : bool , UpperCamelCase__ : list[int] , UpperCamelCase__ : float ) -> int:
'''si... | 332 |
import unittest
from knapsack import knapsack as k
class a ( unittest.TestCase ):
"""simple docstring"""
def UpperCAmelCase_ ( self : List[Any] ) -> List[str]:
"""simple docstring"""
__lowercase = 0
__lowercase = ... | 332 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ : List[str] = logging.get_logger(__name__)
a_ : str = {
'unc-nlp/lxmert-base-uncased': 'https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/config.json',
}
class _sn... | 444 |
a_ : Tuple = {
'A': '.-', 'B': '-...', 'C': '-.-.', 'D': '-..', 'E': '.', 'F': '..-.', 'G': '--.',
'H': '....', 'I': '..', 'J': '.---', 'K': '-.-', 'L': '.-..', 'M': '--', 'N': '-.',
'O': '---', 'P': '.--.', 'Q': '--.-', 'R': '.-.', 'S': '...', 'T': '-', 'U': '..-',
'V': '...... | 444 | 1 |
"""simple docstring"""
from math import pi, sqrt
def lowerCamelCase__ ( __snake_case ) -> float:
"""simple docstring"""
if num <= 0:
raise ValueError('''math domain error''' )
if num > 171.5:
raise OverflowError('''mat... | 19 |
"""simple docstring"""
import argparse
import json
from typing import List
from ltp import LTP
from transformers.models.bert.tokenization_bert import BertTokenizer
def lowerCamelCase__ ( __snake_case ) -> Optional[Any]:
"""simple docstring"""
... | 19 | 1 |
"""simple docstring"""
from __future__ import annotations
__lowerCAmelCase : Union[str, Any] = list[tuple[int, int]]
__lowerCAmelCase : Optional[int] = [
[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, ... | 158 |
"""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
__lowerCAmelCase : Optional[int] = namedtuple(
... | 158 | 1 |
from __future__ import annotations
def lowerCAmelCase_ ( __lowerCamelCase ):
if len(__lowerCamelCase ) == 0:
return array
__snake_case , __snake_case : List[Any] = min(__lowerCamelCase ), max(__lowerCamelCase )
# Compu... | 81 |
import shutil
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import (
is_pt_tf_cross_test,
require_tf,
require_torch,
require_torchvision,
require_vision,
)
from transformers.utils import is_tf_available, is_torch_available, is_vision_available
if is_vision... | 81 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__lowerCamelCase : Dict = {
"configuration_biogpt": ["BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "BioGptConfig"],
"tokenization_biogpt": ["BioGpt... | 457 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
__lowerCamelCase : Any = {
"configuration_mega": ["MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP", "MegaConfig", "MegaOnnxConfig"],
}
try:
if not is_torch_availa... | 457 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ : int = logging.get_logger(__name__)
UpperCAmelCase_ : Dict = {
'''google/vivit-b-16x2-kinetics400''': (
'''https://huggingface.co/google/vivit-b-16x2-kinetics400/resolve/main/co... | 17 |
from collections import defaultdict
def a__ (__lowercase :str , __lowercase :str ) -> bool:
_A : Union[str, Any] = first_str.lower().strip()
_A : int = second_str.lower().strip()
# Remove whitespace
_A : int = first_str.replac... | 206 | 0 |
"""simple docstring"""
import unittest
import torch
from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMixin, UNetTesterMixin
enable_full_determinism()
class ... | 2 |
"""simple docstring"""
def _a ( _SCREAMING_SNAKE_CASE ) -> list:
# bit count represents no. of bits in the gray code
if bit_count < 0:
raise ValueError("""The given input must be positive""" )
# get the generated string sequence
snake_case_ ... | 2 | 1 |
from __future__ import annotations
import math
def __a ( __lowerCAmelCase , __lowerCAmelCase ) -> Optional[int]:
SCREAMING_SNAKE_CASE : List[Any] = u
for i in range(1 , __lowerCAmelCase ):
SCREAMING_SNAKE_CASE : Optional[i... | 352 | """simple docstring"""
import os
import shutil
import sys
import tempfile
import unittest
from pathlib import Path
import pytest
import transformers
from transformers import (
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
AutoTokenizer,
BertConfig,
BertTokenizer,
Be... | 277 | 0 |
from argparse import ArgumentParser
from . import BaseTransformersCLICommand
def lowerCAmelCase__ ( _SCREAMING_SNAKE_CASE : Tuple ):
"""simple docstring"""
return DownloadCommand(args.model , args.cache_dir , args.force , args.trust_remote_code )
... | 547 |
from __future__ import annotations
lowerCamelCase__ = """Muhammad Umer Farooq"""
lowerCamelCase__ = """MIT"""
lowerCamelCase__ = """1.0.0"""
lowerCamelCase__ = """Muhammad Umer Farooq"""
lowerCamelCase__ = """contact@muhammadumerfarooq.me"""
lowerCamelCase__ = """Alpha"""
import re
from html.parser im... | 547 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE = {
... | 94 |
from __future__ import annotations
from typing import Any
class __UpperCamelCase :
'''simple docstring'''
def __init__( self , lowerCamelCase__ ):
UpperCAmelCase__: Optional[int] = num_of_nodes
UpperCAmelCase__: list[list[int]] = []
UpperCAmel... | 113 | 0 |
"""simple docstring"""
# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V an... | 536 | """simple docstring"""
from math import cos, sin, sqrt, tau
from audio_filters.iir_filter import IIRFilter
def UpperCAmelCase ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ = 1 / sqrt(2 ) ):
"""simple docstring"""
A__... | 536 | 1 |
'''simple docstring'''
import os
import tempfile
import unittest
import uuid
from pathlib import Path
from transformers.testing_utils import get_tests_dir, require_soundfile, require_torch, require_vision
from transformers.tools.agent_types import AgentAudio, AgentImage, AgentText
from transformers.utils import is... | 460 |
'''simple docstring'''
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging
lowerCamelCase : str = lo... | 460 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
'MIT/ast-finetuned-audioset-10-10-0.4593': (
'https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593/resolve/main/config.json'
... | 718 |
def _lowerCamelCase ( A_ : int , A_ : int ) -> int:
'''simple docstring'''
return int((input_a, input_a).count(0 ) != 0 )
def _lowerCamelCase ( ) -> None:
'''simple docstring'''
assert nand_gate(0 , 0 ) == 1
assert nand_gate(0 , ... | 582 | 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 = lo... | 655 |
from tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch
from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER, require_tf, require_torch
@require_tor... | 655 | 1 |
import datasets
from .evaluate import evaluate
__SCREAMING_SNAKE_CASE : Any = '\\n@article{hendrycks2021cuad,\n title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},\n author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},\n journal={arXiv p... | 702 | import itertools
from dataclasses import dataclass
from typing import Optional
import pandas as pd
import pyarrow as pa
import datasets
from datasets.table import table_cast
@dataclass
class lowercase_ ( datasets.BuilderConfig ):
_lowerCamelCase = None
class lowercase_ ( datasets.Ar... | 580 | 0 |
# 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-2.0
#
# Unless required by a... | 35 |
import json
import logging
import os
import re
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import datasets
import numpy as np
import torch
import torchaudio
from packaging import version
from torch import nn
import transformers
from transformers import (
... | 322 | 0 |
import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path
import timm
import torch
from huggingface_hub import hf_hub_download
from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImageProcessor
from transformers.uti... | 476 | UpperCAmelCase_ = {
'''Pillow''': '''Pillow<10.0.0''',
'''accelerate''': '''accelerate>=0.20.3''',
'''av''': '''av==9.2.0''',
'''beautifulsoup4''': '''beautifulsoup4''',
'''black''': '''black~=23.1''',
'''codecarbon''': '''codecarbon==1.2.0''',
'''cookiecutter''': '''cook... | 476 | 1 |
'''simple docstring'''
import math
def __UpperCamelCase( _A : int ):
'''simple docstring'''
if not isinstance(_A , _A ):
UpperCAmelCase__ : str = F'''Input value of [number={number}] must be an integer'''
raise TypeError(_A )
if number < 1:
Upp... | 614 | '''simple docstring'''
import argparse
import logging
import pickle
import random
import time
import numpy as np
from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt='%m/%d/%Y %H:%M:%S', level=logging... | 614 | 1 |
import qiskit
def A ( lowercase = 2 ) -> qiskit.result.counts.Counts:
'''simple docstring'''
UpperCamelCase = qubits
# Using Aer's simulator
UpperCamelCase = qiskit.Aer.get_backend('aer_simulator' )
# Creating a Quantum Circuit acting on the q register
UpperCamelCase... | 3 |
from collections.abc import Callable
def A ( lowercase , lowercase , lowercase ) -> float:
'''simple docstring'''
UpperCamelCase = a
UpperCamelCase = b
if function(lowercase ) == 0: # one of the a or b is a root for the function
return a
elif function(lowerc... | 3 | 1 |
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorT... | 68 |
"""simple docstring"""
import os
import tempfile
import unittest
from transformers import NezhaConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...generation.test_utils import Generatio... | 682 | 0 |
'''simple docstring'''
import collections
import gzip
import os
import urllib
import numpy
from tensorflow.python.framework import dtypes, random_seed
from tensorflow.python.platform import gfile
from tensorflow.python.util.deprecation import deprecated
lowercase : Dict = collections.named... | 159 |
'''simple docstring'''
import collections
import inspect
import unittest
from typing import Dict, List, Tuple
from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_available
fr... | 159 | 1 |
'''simple docstring'''
import logging
import numpy as np
import pytest
from scipy.linalg import eigh
logging.basicConfig(level=logging.INFO, format='''%(message)s''')
def __a ( _UpperCamelCase: Any ) -> np.ndarray:
"""simple docstring"""
return input_array.re... | 185 |
def __a ( __lowerCAmelCase ) -> List[str]:
stooge(__lowerCAmelCase , 0 , len(__lowerCAmelCase ) - 1 )
return arr
def __a ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> int:
if i >= h:
return
... | 352 | 0 |
'''simple docstring'''
import numpy as np
def UpperCamelCase__ ( _lowercase : np.array ) -> np.array:
return 1 / (1 + np.exp(-vector ))
if __name__ == "__main__":
import doctest
doctest.testmod() | 466 | '''simple docstring'''
def UpperCamelCase__ ( _lowercase : int ) -> int:
if not isinstance(_lowercase , _lowercase ):
__UpperCAmelCase: List[str] = F'''Input value of [number={number}] must be an integer'''
raise TypeError(_lowercase )
if number < 1:
__... | 466 | 1 |
'''simple docstring'''
def A (__lowerCamelCase :str ):
_lowerCAmelCase = len(__lowerCamelCase )
while cur > 1:
# Find the maximum number in arr
_lowerCAmelCase = arr.index(max(arr[0:cur] ) )
# Reverse from 0 to mi
_low... | 5 |
"""simple docstring"""
import collections
import os
from typing import List, Optional, Tuple
from transformers.utils import is_jieba_available, requires_backends
if is_jieba_available():
import jieba
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
SCREAMING_SNAKE_CASE... | 156 | 0 |
"""simple docstring"""
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
fr... | 712 |
"""simple docstring"""
from __future__ import annotations
from functools import lru_cache
from math import ceil
SCREAMING_SNAKE_CASE : str = 100
SCREAMING_SNAKE_CASE : str = set(range(3, NUM_PRIMES, 2))
primes.add(2)
SCREAMING_SNAKE_CASE : int
for prime in... | 229 | 0 |
'''simple docstring'''
import torch
from diffusers import DDIMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class __UpperCAmelCase ( __a ):
__A : Dict = (DDIMParallelScheduler,)
__A : int = (('eta', 0.0), ('num_inference_steps', 50))
... | 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 inspect
import unittest
from transformers import RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_c... | 703 |
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()
__snake_case : str = [
"""word_embeddings_laye... | 365 | 0 |
import io
import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_UpperCAmelCase = logging.get_logger(__name__)
_UpperCAmelCase = '▁'
... | 558 |
import random
from .binary_exp_mod import bin_exp_mod
def A ( _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE=1000 ) -> Optional[int]:
if n < 2:
return False
if n % 2 == 0:
return n == 2
# this means n is odd
lowerCamelCa... | 311 | 0 |
'''simple docstring'''
from __future__ import annotations
import math
import numpy as np
from numpy.linalg import norm
def SCREAMING_SNAKE_CASE ( lowercase_ : Optional[Any] , lowercase_ : Tuple ):
return math.sqrt(sum(pow(a - b , 2 ) for a, b in zip(lowerca... | 703 |
'''simple docstring'''
import json
import logging
import os
import socket
import git
import numpy as np
import torch
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d - %(message)s''',
datefmt='''%m/%d/%Y %H:%M:%S''',
level=logging.INFO,
)
lowercase_ : ... | 653 | 0 |
'''simple docstring'''
import os
def UpperCamelCase ( ) -> str:
'''simple docstring'''
with open(os.path.dirname(lowercase_ ) + '''/grid.txt''' ) as f:
lowercase =[] # noqa: E741
for _ in range(2_0 ):
l.append([int(lowercase_ ) for x in f.readline().split()] )
lowe... | 72 |
'''simple docstring'''
def a__ ( UpperCamelCase_ : int | float | str ):
try:
UpperCAmelCase__ :Union[str, Any] = float(UpperCamelCase_ )
except ValueError:
raise ValueError('''Please enter a valid number''' )
UpperCAmelCase__ :List[str] ... | 467 | 0 |
"""simple docstring"""
from math import factorial
def lowerCAmelCase_ ( UpperCamelCase__ : int , UpperCamelCase__ : int , UpperCamelCase__ : float ):
"""simple docstring"""
if successes > trials:
raise ValueError("""successes must be lower or equal to tri... | 442 |
"""simple docstring"""
# Algorithm for the pigeonhole sorting
def lowerCAmelCase_ ( UpperCamelCase__ : Dict ):
"""simple docstring"""
__lowercase = min(UpperCamelCase__ ) # min() finds the minimum value
__lowercase = max(UpperCamelCase__ ) # ma... | 442 | 1 |
from __future__ import annotations
from dataclasses import dataclass
@dataclass
class _A :
SCREAMING_SNAKE_CASE : float
SCREAMING_SNAKE_CASE : TreeNode | None = None
SCREAMING_SNAKE_CASE : TreeNode | None = None
def A_ ( a ):
"""simple docstring"""
def is_... | 511 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCAmelCase : Optional[Any] = {
'configuration_mobilebert': [
'MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP',
... | 511 | 1 |
'''simple docstring'''
import argparse
import torch
from transformers import BertForMaskedLM
if __name__ == "__main__":
_UpperCAmelCase = argparse.ArgumentParser(
description=(
'Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Lear... | 715 |
_UpperCAmelCase = {
'a': 'AAAAA',
'b': 'AAAAB',
'c': 'AAABA',
'd': 'AAABB',
'e': 'AABAA',
'f': 'AABAB',
'g': 'AABBA',
'h': 'AABBB',
'i': 'ABAAA',
'j': 'BBBAA',
'k': 'ABAAB',
'l': 'ABABA',
'm': 'ABABB',
'n': 'ABBAA',
'o': 'ABBAB',
'p': 'ABBB... | 371 | 0 |
'''simple docstring'''
def _snake_case ( A = 3 , A = 7 , A = 1000000 ) -> int:
lowerCAmelCase__ = 0
lowerCAmelCase__ = 1
for current_denominator in range(1 , limit + 1 ):
lowerCAmelCase__ = ... | 90 |
"""simple docstring"""
import importlib
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Union
import torch
from ..utils import BaseOutput
SCREAMING_SNAKE_CASE_ = '''scheduler_config.json'''
class _UpperCAmelCase ( SCREAMI... | 373 | 0 |
"""simple docstring"""
import tempfile
import unittest
from make_student import create_student_by_copying_alternating_layers
from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch
__SCREAMING_SNAKE_CASE ="sshleifer/b... | 477 | """simple docstring"""
import os
import random
import sys
from . import cryptomath_module as cryptoMath # noqa: N812
from . import rabin_miller as rabinMiller # noqa: N812
def lowercase__( ):
print('Making key files...' )
make_key_files('rsa' , 10_24 )
print('Key files generation... | 477 | 1 |
'''simple docstring'''
from math import atan, cos, radians, sin, tan
from .haversine_distance import haversine_distance
UpperCAmelCase__ : Optional[int] = 6_3_7_8_1_3_7.0
UpperCAmelCase__ : Any = 6_3_5_6_7_5_2.3_1_4_2_4_5
UpperCAmelCase__ : List[str] = 6_37_81_37
def A... | 48 |
'''simple docstring'''
def A ( UpperCamelCase_ : str , UpperCamelCase_ : int ) -> list:
'''simple docstring'''
lowerCAmelCase__ = word.split()
def justify(UpperCamelCase_ : list , UpperCamelCase_ : int , UpperCamelCa... | 48 | 1 |
'''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
a_ = logging.get_logger(__name__)
a_ = {"v... | 705 |
'''simple docstring'''
import inspect
import unittest
from transformers import ConvNextVaConfig
from transformers.models.auto import get_values
from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES
from transformers.testing_utils import require_torch, require_vision,... | 92 | 0 |
'''simple docstring'''
import collections
import inspect
import unittest
from transformers import SwinvaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configurat... | 42 | import argparse
import os
import shutil
import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
def __UpperCamelCase ( A ):
UpperCamelCase__ = args.pruning_method
UpperCamelCase__ = args.threshold
UpperCame... | 415 | 0 |
"""simple docstring"""
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING:
import torch
class _UpperCAmelCase ( ... | 710 |
"""simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE : str = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE : Optional[Any] = {
"""asapp/sew-d-tiny-100... | 229 | 0 |
'''simple docstring'''
import math
import qiskit
def A_( A : int = 1 , A : int = 1 , A : int = 1):
if (
isinstance(A , A)
or isinstance(A , A)
or isinstance(A , A)
):
raise TypeError(... | 3 |
'''simple docstring'''
import inspect
import os
import unittest
from dataclasses import dataclass
import torch
from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_... | 161 | 0 |
'''simple docstring'''
from scipy.stats import pearsonr
import datasets
__magic_name__ = '\nPearson correlation coefficient and p-value for testing non-correlation.\nThe Pearson correlation coefficient measures the linear relationship between two datasets. The calculation of the p-... | 712 |
# 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 requi... | 314 | 0 |
"""simple docstring"""
from dataclasses import dataclass
from typing import Optional
import numpy as np
import torch
import torch.nn as nn
from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_u... | 473 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
from diffusers.utils import floats_tensor,... | 536 | 0 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Tuple
import numpy as np
import torch
@dataclass
class lowercase :
"""simple docstring"""
_a = 42 # [batch_size x 3]
_a = 42 # [batch_size x 3]
_a = 42 # [batch_size x 3]
_a ... | 280 |
'''simple docstring'''
class lowercase :
"""simple docstring"""
def __init__( self , UpperCamelCase_ ):
'''simple docstring'''
UpperCamelCase__ :Union[str, Any] = n
UpperCamelCase__ :Tuple = [None] * self.n
UpperCamelCase... | 280 | 1 |
"""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 ... | 549 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
_SCREAMING_SNAKE_CASE : List[Any] = {'''conf... | 549 | 1 |
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class __SCREAMING_SNAKE_CASE( a_ , a_ ):
@register_to_config
def __init__( self: List[Any] , *,
UpperCamelCase: int ... | 719 |
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_trust_remote_... | 372 | 0 |
"""simple docstring"""
import math
def a ( __UpperCAmelCase : list , __UpperCAmelCase : int ) -> int:
__magic_name__: str = len(__UpperCAmelCase )
__magic_name__: Optional[int] = int(math.floor(math.sqrt(__U... | 96 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
snake_case : int = {}
try:
if not is_sentencepiece_available... | 545 | 0 |
'''simple docstring'''
import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey
class __SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__( self , snake_case_ , snake_case_ , snake_case_ ):
'''simple docstring'''
if dst_width < 0 or d... | 389 | '''simple docstring'''
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class __... | 389 | 1 |
import argparse
import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers import (
CLIPTokenizer,
CLIPTokenizerFast,
VideoMAEImageProcessor,
XCLIPConfig,
XCLIPModel,
XCLIPProcessor,
XCLIPTextConfig,
XCLIPVisionCo... | 21 |
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
lowerCamelCase : Optional[Any] = logging.get_logger(__name__)
lowerCamelCase ... | 170 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowercase = logging.get_logger(__name__)
__lowercase = {
# See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}
class lowerCamelCase_ ( UpperCAmelCase_ ):
... | 452 | import unittest
from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipel... | 452 | 1 |
from __future__ import annotations
import math
def _lowerCamelCase ( __A : int ) -> int:
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even nu... | 485 |
from __future__ import annotations
from collections.abc import MutableSequence
class UpperCAmelCase_ :
"""simple docstring"""
def __init__( self: List[Any] , _UpperCAmelCase: int , _UpperCAmelCase: MutableSequence[float] ):
if len(_UpperCAmelCase ) != degree... | 687 | 0 |
'''simple docstring'''
from __future__ import annotations
from functools import lru_cache
from math import ceil
a = 100
a = set(range(3, NUM_PRIMES, 2))
primes.add(2)
a = 42
for prime in range(3, ceil(NUM_PRIMES**0.5), 2):
if prime not in... | 347 |
'''simple docstring'''
import json
import os
import unittest
from transformers import AutoTokenizer, GPTaTokenizer, GPTaTokenizerFast
from transformers.models.gpta.tokenization_gpta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common i... | 347 | 1 |
import argparse
import logging
import os
import time
import timeit
import datasets
import numpy as np
import pycuda.autoinit # noqa: F401
import pycuda.driver as cuda
import tensorrt as trt
import torch
from absl import logging as absl_logging
from accelerate import Accelerator
from datasets imp... | 623 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableDiffusionUpscalePipeline, UNetaDConditionModel
from diffusers.u... | 623 | 1 |
"""simple docstring"""
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
BERT_START_DOCSTRING,
Ber... | 714 |
"""simple docstring"""
import argparse
import copy
def lowerCamelCase (a_ :Union[str, Any]) -> Tuple:
lowercase :Dict = {}
with open(a_) as f:
for line in f:
if line.split()[0] not in dict_of_neighbours:
... | 475 | 0 |
"""simple docstring"""
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 impo... | 642 |
"""simple docstring"""
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
class __UpperCAmelCase ( unittest.TestCase , _UpperCamelCase ):
def UpperCAmelCase ( self : Dict ) -> List[Any]:
'''simple do... | 642 | 1 |
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ ):
if discount_rate < 0:
raise ValueError("Discount rate cannot be negative" )
if not cash_flows:
raise ValueError("Cash flows list cannot be empty" )
lowerCamelCase_ = sum(
cash_flow ... | 706 |
import unittest
from .lib import (
Matrix,
Vector,
axpy,
square_zero_matrix,
unit_basis_vector,
zero_vector,
)
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
def SCREAMING_SNAKE_CASE_( self ) -> None:
lowerCamelCase_ = Vector([1, 2, 3] ... | 313 | 0 |
"""simple docstring"""
import platform
from argparse import ArgumentParser
import huggingface_hub
from .. import __version__ as version
from ..utils import is_accelerate_available, is_torch_available, is_transformers_available, is_xformers_available
from . import BaseDiffusersCLICommand
def ... | 552 |
"""simple docstring"""
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F
from transformers import DetrImageProcessor, TableTransformerConfig, TableTransforme... | 555 | 0 |
# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile
from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version import Version
UpperCamelCase_... | 714 |
import argparse
import os
from pathlib import Path
import torch
from bark.generation import _load_model as _bark_load_model
from huggingface_hub import hf_hub_download
from transformers import EncodecConfig, EncodecModel, set_seed
from transformers.models.bark.configuration_bark import (
BarkCoarseConfig,
... | 381 | 0 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.