code stringlengths 81 54k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
'''simple docstring'''
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device
snake_case_ = False
class SCREAMING_... | 707 |
'''simple docstring'''
import re
import string
import numpy as np
import datasets
snake_case_ = '\nReturns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.\n'
snake_case_ = '\nArg... | 68 | 0 |
import unittest
from transformers import TrOCRConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ... | 708 |
'''simple docstring'''
import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, loggi... | 68 | 0 |
'''simple docstring'''
import comet # From: unbabel-comet
import torch
import datasets
snake_case_ = datasets.logging.get_logger(__name__)
snake_case_ = '\\n@inproceedings{rei-EtAl:2020:WMT,\n author = {Rei, Ricardo and Stewart, Craig and Farinha, Ana C and Lavie... | 709 |
'''simple docstring'''
import re
from filelock import FileLock
try:
import nltk
snake_case_ = True
except (ImportError, ModuleNotFoundError):
snake_case_ = False
if NLTK_AVAILABLE:
with FileLock('.lock') as lock:
nltk.download('punkt'... | 68 | 0 |
'''simple docstring'''
def __lowerCamelCase ( SCREAMING_SNAKE_CASE_ : int = 3 , SCREAMING_SNAKE_CASE_ : int = 7 , SCREAMING_SNAKE_CASE_ : int = 1_0_0_0_0_0_0 ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Dict = 0
... | 710 |
'''simple docstring'''
from __future__ import annotations
import copy
import inspect
import unittest
import numpy as np
from transformers import is_tf_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
fro... | 68 | 0 |
'''simple docstring'''
import argparse
import csv
import logging
import os
import random
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange
from transformers import (
CONFIG_NAME,
... | 711 |
'''simple docstring'''
def __lowerCamelCase ( SCREAMING_SNAKE_CASE_ : List[str] , SCREAMING_SNAKE_CASE_ : Tuple ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : List[Any] = [1]
for i in range(2 , SCREAMING... | 68 | 0 |
'''simple docstring'''
import json
import os
from typing import Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
snake_case_ : Optional[int] = logging.get_logger(__name__)
snake_case_ : List[str] ... | 712 |
'''simple docstring'''
import argparse
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt
if __name__ == "__main__":
snake_case_ = argparse.ArgumentParser()
parser.add_argument(
'--checkpoint_path', default... | 68 | 0 |
'''simple docstring'''
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, requi... | 713 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
snake_case_ = logging.get_logger(__name__)
snake_case_ = {
'camembert-ba... | 68 | 0 |
'''simple docstring'''
import hashlib
import unittest
from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nest... | 714 |
'''simple docstring'''
def __lowerCamelCase ( SCREAMING_SNAKE_CASE_ : list[int] ) -> list[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : List[str] = len(SCREAMING_SNAKE_CASE_ )
for i in range(SCREAMING_SNAKE_CASE_ ):
fo... | 68 | 0 |
def __lowerCamelCase ( SCREAMING_SNAKE_CASE_ : list[int] ) -> list[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : List[str] = len(SCREAMING_SNAKE_CASE_ )
for i in range(SCREAMING_SNAKE_CASE_ ):
for j in range(i + 1 , SCREA... | 715 |
'''simple docstring'''
import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch
from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search
from transformers.testing_utils import CaptureStdout, TestCase... | 68 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
snake_case_ = {
'configuration_informer': [
'INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
... | 716 |
'''simple docstring'''
from __future__ import annotations
import numpy as np
from numpy import floataa
from numpy.typing import NDArray
def __lowerCamelCase ( SCREAMING_SNAKE_CASE_ : NDArray[floataa] , SCREAMING_SNAKE_CASE_ : NDArray[floataa] , SCREAMING_S... | 68 | 0 |
'''simple docstring'''
import math
def __lowerCamelCase ( SCREAMING_SNAKE_CASE_ : int ) -> bool:
"""simple docstring"""
assert isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) and (
number >= 0
), "'number' must ... | 717 |
'''simple docstring'''
def __lowerCamelCase ( SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int ) -> int:
"""simple docstring"""
return 1 if input_a == input_a else 0
def __lowerCamelCase ( ) -> None:
"""s... | 68 | 0 |
'''simple docstring'''
from __future__ import annotations
from random import random
from typing import Generic, TypeVar
snake_case_ = TypeVar('KT')
snake_case_ = TypeVar('VT')
class SCREAMING_SNAKE_CASE__ ( Generic[KT, VT] ):
def __init__( self ... | 718 |
'''simple docstring'''
def __lowerCamelCase ( SCREAMING_SNAKE_CASE_ : float , SCREAMING_SNAKE_CASE_ : float , SCREAMING_SNAKE_CASE_ : float , SCREAMING_SNAKE_CASE_ : float , SCREAMING_SNAKE_CASE_ : float , ) -> float:... | 68 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
snake_case_ : Dict = {'configuration_fnet': ['FNET_PRETRA... | 719 |
'''simple docstring'''
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.generation import DisjunctiveConstraint
@require_torch
class SCREAMING_SNAKE_CASE... | 68 | 0 |
'''simple docstring'''
import tempfile
import unittest
from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
torch_device,
)
from ...generation.test_utils import Ge... | 720 |
'''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_det... | 68 | 0 |
'''simple docstring'''
import argparse
import torch
from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging
logging.set_verbosity_info()
def __lowerCamelCase ( SCREAMING_SNAKE_CASE_ : Union[... | 721 |
'''simple docstring'''
import os
import unittest
from huggingface_hub.utils import are_progress_bars_disabled
import transformers.models.bart.tokenization_bart
from transformers import logging
from transformers.testing_utils import CaptureLogger, mockenv, mockenv_context
from transformers.uti... | 68 | 0 |
'''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
if is_torch_ava... | 700 |
'''simple docstring'''
import json
import os
import tempfile
from unittest.mock import patch
import torch
from torch.utils.data import DataLoader, TensorDataset
from accelerate import DistributedType, infer_auto_device_map, init_empty_weights
from accelerate.accelerator import Accelerator
f... | 68 | 0 |
'''simple docstring'''
import argparse
import os
from accelerate.test_utils import execute_subprocess_async
def __lowerCamelCase ( SCREAMING_SNAKE_CASE_ : Tuple=None ) -> Dict:
"""simple docstring"""
if subparsers is not None:
SCREAMING_... | 701 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
snake_case_ = logging.get_logger(__name__)
snake_case_ = {
'facebook/xmo... | 68 | 0 |
'''simple docstring'''
import argparse
import torch
from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
snake_case_ = logging.get_logger(__name__)
snake_case_ = [
['attention... | 702 |
'''simple docstring'''
def __lowerCamelCase ( SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int ) -> int:
"""simple docstring"""
return int((input_a, input_a).count(1 ) != 0 )
def __lowerCamelCase ( ) -> ... | 68 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable
snake_case_ = {
'configuration_gpt_neox_japanese': ['GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTNeoXJapan... | 703 |
'''simple docstring'''
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig
snake_case_ = logging.get_logger(__name__)
snake_case_ = {
'Intel/dpt-large': 'https://huggingface.co/Intel/dpt-large/resolve/m... | 68 | 0 |
'''simple docstring'''
from typing import List, Union
import numpy as np
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
... | 704 |
'''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
if is_torch_ava... | 68 | 0 |
'''simple docstring'''
def __lowerCamelCase ( SCREAMING_SNAKE_CASE_ : float , SCREAMING_SNAKE_CASE_ : float , SCREAMING_SNAKE_CASE_ : int ) -> float:
"""simple docstring"""
if principal <= 0:
raise Exception("Principal b... | 705 |
'''simple docstring'''
from __future__ import annotations
def __lowerCamelCase ( SCREAMING_SNAKE_CASE_ : int ) -> bool:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[Any] = str(SCREAMING_SNAKE_CASE_ )
return len(SCREAMING_SNAKE... | 68 | 0 |
'''simple docstring'''
import argparse
from collections import defaultdict
import yaml
snake_case_ = 'docs/source/en/_toctree.yml'
def __lowerCamelCase ( SCREAMING_SNAKE_CASE_ : List[Any] ) -> Optional[Any]:
"""simple docstring"""
... | 706 |
'''simple docstring'''
from typing import Optional
import pyspark
from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader
class SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase ):
... | 68 | 0 |
'''simple docstring'''
def __lowerCamelCase ( SCREAMING_SNAKE_CASE_ : str ) -> list:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : int = [0] * len(SCREAMING_SNAKE_CASE_ )
for i in range(1 , len(SCREAMING_SNAKE_CASE_ ) ):
... | 707 |
'''simple docstring'''
import re
import string
import numpy as np
import datasets
snake_case_ = '\nReturns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.\n'
snake_case_ = '\nArg... | 68 | 0 |
from __future__ import annotations
import math
from collections.abc import Callable
def __lowerCamelCase ( SCREAMING_SNAKE_CASE_ : Callable[[int | float], int | float] , SCREAMING_SNAKE_CASE_ : int | float , SCREAMING_SNAKE_CASE_ : int | float , SCREAMING_S... | 708 |
'''simple docstring'''
import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, loggi... | 68 | 0 |
'''simple docstring'''
from __future__ import annotations
def __lowerCamelCase ( SCREAMING_SNAKE_CASE_ : list[int] , SCREAMING_SNAKE_CASE_ : list[int] , SCREAMING_SNAKE_CASE_ : list[int] , SCREAMING_SNAKE_CASE_ : list[list[str]] , SC... | 709 |
'''simple docstring'''
import re
from filelock import FileLock
try:
import nltk
snake_case_ = True
except (ImportError, ModuleNotFoundError):
snake_case_ = False
if NLTK_AVAILABLE:
with FileLock('.lock') as lock:
nltk.download('punkt'... | 68 | 0 |
'''simple docstring'''
import random
import unittest
import numpy as np
import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax
if is_flax_available():
import os
import jax.... | 710 |
'''simple docstring'''
from __future__ import annotations
import copy
import inspect
import unittest
import numpy as np
from transformers import is_tf_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
fro... | 68 | 0 |
'''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_... | 711 |
'''simple docstring'''
def __lowerCamelCase ( SCREAMING_SNAKE_CASE_ : List[str] , SCREAMING_SNAKE_CASE_ : Tuple ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : List[Any] = [1]
for i in range(2 , SCREAMING... | 68 | 0 |
'''simple docstring'''
snake_case_ : str = 8.3_1_4_4_6_2 # Unit - J mol-1 K-1
def __lowerCamelCase ( SCREAMING_SNAKE_CASE_ : float , SCREAMING_SNAKE_CASE_ : float , SCREAMING_SNAKE_CASE_ : float ) -> float:
"""simple docstr... | 712 |
'''simple docstring'''
import argparse
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt
if __name__ == "__main__":
snake_case_ = argparse.ArgumentParser()
parser.add_argument(
'--checkpoint_path', default... | 68 | 0 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import BlenderbotSmallConfig, BlenderbotSmallTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ... | 713 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
snake_case_ = logging.get_logger(__name__)
snake_case_ = {
'camembert-ba... | 68 | 0 |
'''simple docstring'''
from __future__ import annotations
def __lowerCamelCase ( SCREAMING_SNAKE_CASE_ : int ) -> bool:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[Any] = str(SCREAMING_SNAKE_CASE_ )
return len(SCREAMING_SNAKE... | 714 |
'''simple docstring'''
def __lowerCamelCase ( SCREAMING_SNAKE_CASE_ : list[int] ) -> list[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : List[str] = len(SCREAMING_SNAKE_CASE_ )
for i in range(SCREAMING_SNAKE_CASE_ ):
fo... | 68 | 0 |
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_configuration_common import ... | 715 |
'''simple docstring'''
import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch
from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search
from transformers.testing_utils import CaptureStdout, TestCase... | 68 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
snake_case_ = {
'configuration_bridgetower': [
'BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'BridgeTowe... | 716 |
'''simple docstring'''
from __future__ import annotations
import numpy as np
from numpy import floataa
from numpy.typing import NDArray
def __lowerCamelCase ( SCREAMING_SNAKE_CASE_ : NDArray[floataa] , SCREAMING_SNAKE_CASE_ : NDArray[floataa] , SCREAMING_S... | 68 | 0 |
'''simple docstring'''
import argparse
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt
if __name__ == "__main__":
snake_case_ = argparse.ArgumentParser()
parser.add_argument(
'--checkpoint_path', default... | 717 |
'''simple docstring'''
def __lowerCamelCase ( SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int ) -> int:
"""simple docstring"""
return 1 if input_a == input_a else 0
def __lowerCamelCase ( ) -> None:
"""s... | 68 | 0 |
'''simple docstring'''
from ... import PretrainedConfig
snake_case_ = {
'sijunhe/nezha-cn-base': 'https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json',
}
class SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase ):
_A = NEZHA_PRETRAINED_CONFIG_... | 718 |
'''simple docstring'''
def __lowerCamelCase ( SCREAMING_SNAKE_CASE_ : float , SCREAMING_SNAKE_CASE_ : float , SCREAMING_SNAKE_CASE_ : float , SCREAMING_SNAKE_CASE_ : float , SCREAMING_SNAKE_CASE_ : float , ) -> float:... | 68 | 0 |
'''simple docstring'''
from json import JSONDecodeError # Workaround for requests.exceptions.JSONDecodeError
import requests
def __lowerCamelCase ( SCREAMING_SNAKE_CASE_ : str = "isbn/0140328726" ) -> dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE... | 719 |
'''simple docstring'''
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.generation import DisjunctiveConstraint
@require_torch
class SCREAMING_SNAKE_CASE... | 68 | 0 |
'''simple docstring'''
import numpy as np
import qiskit
def __lowerCamelCase ( SCREAMING_SNAKE_CASE_ : int = 8 , SCREAMING_SNAKE_CASE_ : int | None = None ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : str = np.random.defaul... | 720 |
'''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_det... | 68 | 0 |
'''simple docstring'''
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 tr... | 721 |
'''simple docstring'''
import os
import unittest
from huggingface_hub.utils import are_progress_bars_disabled
import transformers.models.bart.tokenization_bart
from transformers import logging
from transformers.testing_utils import CaptureLogger, mockenv, mockenv_context
from transformers.uti... | 68 | 0 |
'''simple docstring'''
import unittest
from knapsack import knapsack as k
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
def __lowerCamelCase ( self ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : D... | 700 |
'''simple docstring'''
import json
import os
import tempfile
from unittest.mock import patch
import torch
from torch.utils.data import DataLoader, TensorDataset
from accelerate import DistributedType, infer_auto_device_map, init_empty_weights
from accelerate.accelerator import Accelerator
f... | 68 | 0 |
'''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... | 701 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
snake_case_ = logging.get_logger(__name__)
snake_case_ = {
'facebook/xmo... | 68 | 0 |
'''simple docstring'''
import argparse
import copy
def __lowerCamelCase ( SCREAMING_SNAKE_CASE_ : Optional[Any] ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Any = {}
with open(SCREAMING_SNAKE_CASE_ ) as f:
f... | 702 |
'''simple docstring'''
def __lowerCamelCase ( SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int ) -> int:
"""simple docstring"""
return int((input_a, input_a).count(1 ) != 0 )
def __lowerCamelCase ( ) -> ... | 68 | 0 |
'''simple docstring'''
import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, Sch... | 703 |
'''simple docstring'''
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig
snake_case_ = logging.get_logger(__name__)
snake_case_ = {
'Intel/dpt-large': 'https://huggingface.co/Intel/dpt-large/resolve/m... | 68 | 0 |
'''simple docstring'''
import logging
import os
import sys
import warnings
from dataclasses import dataclass, field
from random import randint
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import DatasetDict, load_dataset
import transformers... | 704 |
'''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
if is_torch_ava... | 68 | 0 |
'''simple docstring'''
def __lowerCamelCase ( SCREAMING_SNAKE_CASE_ : int ) -> int:
"""simple docstring"""
if not isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
raise TypeError("only integers accepted as input" )
... | 705 |
'''simple docstring'''
from __future__ import annotations
def __lowerCamelCase ( SCREAMING_SNAKE_CASE_ : int ) -> bool:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[Any] = str(SCREAMING_SNAKE_CASE_ )
return len(SCREAMING_SNAKE... | 68 | 0 |
'''simple docstring'''
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
snake_case_ =... | 706 |
'''simple docstring'''
from typing import Optional
import pyspark
from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader
class SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase ):
... | 68 | 0 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_ut... | 707 |
'''simple docstring'''
import re
import string
import numpy as np
import datasets
snake_case_ = '\nReturns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.\n'
snake_case_ = '\nArg... | 68 | 0 |
from __future__ import annotations
def __lowerCamelCase ( SCREAMING_SNAKE_CASE_ : float , SCREAMING_SNAKE_CASE_ : float , SCREAMING_SNAKE_CASE_ : float , ) -> tuple:
"""simple docstring"""
if (electron_conc, hole_conc, intrinsic_conc).... | 708 |
'''simple docstring'''
import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, loggi... | 68 | 0 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
UniSpeechConfig,
UniSpeechForCTC,
UniSpeechForPreTraining,
WavaVecaFeatureExtractor,
WavaVecaPhonemeCTCTokenizer,
... | 709 |
'''simple docstring'''
import re
from filelock import FileLock
try:
import nltk
snake_case_ = True
except (ImportError, ModuleNotFoundError):
snake_case_ = False
if NLTK_AVAILABLE:
with FileLock('.lock') as lock:
nltk.download('punkt'... | 68 | 0 |
'''simple docstring'''
from typing import List, Optional, Tuple, Union
import PIL
import torch
from torchvision import transforms
from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor... | 710 |
'''simple docstring'''
from __future__ import annotations
import copy
import inspect
import unittest
import numpy as np
from transformers import is_tf_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
fro... | 68 | 0 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import is_flax_available
from transformers.testing_utils import require_flax
from ..test_modeling_flax_common import ids_tensor
if is_flax_available():
import jax
import jax.numpy as jnp
fr... | 711 |
'''simple docstring'''
def __lowerCamelCase ( SCREAMING_SNAKE_CASE_ : List[str] , SCREAMING_SNAKE_CASE_ : Tuple ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : List[Any] = [1]
for i in range(2 , SCREAMING... | 68 | 0 |
'''simple docstring'''
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 SCREAMI... | 712 |
'''simple docstring'''
import argparse
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt
if __name__ == "__main__":
snake_case_ = argparse.ArgumentParser()
parser.add_argument(
'--checkpoint_path', default... | 68 | 0 |
'''simple docstring'''
import numpy as np
class SCREAMING_SNAKE_CASE__ :
def __init__( self ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[int] = (0, 0)
SCREAMING_SNAKE_CASE_ : Optional[Any] = N... | 713 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
snake_case_ = logging.get_logger(__name__)
snake_case_ = {
'camembert-ba... | 68 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case_ = logging.get_logger(__name__)
snake_case_ = {'ctrl': 'https://huggingface.co/ctrl/resolve/main/config.json'}
class SCREAMING_SNAKE_CASE__ ( _UpperCAme... | 714 |
'''simple docstring'''
def __lowerCamelCase ( SCREAMING_SNAKE_CASE_ : list[int] ) -> list[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : List[str] = len(SCREAMING_SNAKE_CASE_ )
for i in range(SCREAMING_SNAKE_CASE_ ):
fo... | 68 | 0 |
import re
import string
import numpy as np
import datasets
snake_case_ = '\nReturns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.\n'
snake_case_ = '\nArgs:\n predictions: List of predict... | 715 |
'''simple docstring'''
import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch
from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search
from transformers.testing_utils import CaptureStdout, TestCase... | 68 | 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_distilbert import DistilBertTokenizer
snake_case_ = logging.... | 716 |
'''simple docstring'''
from __future__ import annotations
import numpy as np
from numpy import floataa
from numpy.typing import NDArray
def __lowerCamelCase ( SCREAMING_SNAKE_CASE_ : NDArray[floataa] , SCREAMING_SNAKE_CASE_ : NDArray[floataa] , SCREAMING_S... | 68 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
snake_case_ = logging.get_logger(__name__)
snake_case_ = {
'google/bit... | 717 |
'''simple docstring'''
def __lowerCamelCase ( SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int ) -> int:
"""simple docstring"""
return 1 if input_a == input_a else 0
def __lowerCamelCase ( ) -> None:
"""s... | 68 | 0 |
'''simple docstring'''
import random
from .binary_exp_mod import bin_exp_mod
def __lowerCamelCase ( SCREAMING_SNAKE_CASE_ : Any , SCREAMING_SNAKE_CASE_ : Dict=1_0_0_0 ) -> Any:
"""simple docstring"""
if n < 2:
return False
... | 718 |
'''simple docstring'''
def __lowerCamelCase ( SCREAMING_SNAKE_CASE_ : float , SCREAMING_SNAKE_CASE_ : float , SCREAMING_SNAKE_CASE_ : float , SCREAMING_SNAKE_CASE_ : float , SCREAMING_SNAKE_CASE_ : float , ) -> float:... | 68 | 0 |
'''simple docstring'''
def __lowerCamelCase ( SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int ) -> int:
"""simple docstring"""
return int((input_a, input_a).count(1 ) != 0 )
def __lowerCamelCase ( ) -> ... | 719 |
'''simple docstring'''
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.generation import DisjunctiveConstraint
@require_torch
class SCREAMING_SNAKE_CASE... | 68 | 0 |
'''simple docstring'''
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 impor... | 720 |
'''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_det... | 68 | 0 |
'''simple docstring'''
import numpy as np
import torch
from torch.utils.data import Dataset
from utils import logger
class SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase ):
def __init__( self , lowercase__ , lowercase__ ):
"""simpl... | 721 |
'''simple docstring'''
import os
import unittest
from huggingface_hub.utils import are_progress_bars_disabled
import transformers.models.bart.tokenization_bart
from transformers import logging
from transformers.testing_utils import CaptureLogger, mockenv, mockenv_context
from transformers.uti... | 68 | 0 |
from collections.abc import Callable
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> float:
lowerCAmelCase__ : float = a
lowerCAmelCase__ : float = b
if function(SCREAMING_SNAKE_CASE_ ) == 0: # one of... | 69 |
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class A__ ( __magic_name__ ):
... | 69 | 1 |
from argparse import ArgumentParser
from .env import EnvironmentCommand
def lowerCAmelCase__ ( ) -> Any:
lowerCAmelCase__ : List[str] = ArgumentParser('Diffusers CLI tool' , usage='diffusers-cli <command> [<args>]' )
lowerCAmelCase__ : Optional[int] = parser.... | 69 |
import unittest
from transformers import SqueezeBertConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_ten... | 69 | 1 |
from __future__ import annotations
class A__ :
def __init__( self : Any , a : int = 0 ):
'''simple docstring'''
lowerCAmelCase__ : Any = key
def _lowerCamelCase ( self : ... | 69 |
lowerCamelCase__ = """Alexander Joslin"""
import operator as op
from .stack import Stack
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ ) -> int:
lowerCAmelCase__ : Union[str, Any] = {'*': op.mul, '/': op.truediv, '+': op.add, '-': op.sub}
lowerCAmelCase__ : Sta... | 69 | 1 |
import itertools
import os
from collections import Counter, defaultdict
from concurrent.futures import ThreadPoolExecutor, as_completed
import numpy as np
import datasets
from .execute import check_correctness
lowerCamelCase__ = """\
@misc{chen2021evaluating,
title={Evaluating Large Lang... | 69 |
import numpy
class A__ :
def __init__( self : Tuple , a : numpy.ndarray , a : numpy.ndarray ):
'''simple docstring'''
lowerCAmelCase__ : int = input_array
# Random initial weights... | 69 | 1 |
from typing import List
import jiwer
import jiwer.transforms as tr
from packaging import version
import datasets
from datasets.config import PY_VERSION
if PY_VERSION < version.parse("""3.8"""):
import importlib_metadata
else:
import importlib.metadata as importlib_metadata
lowerCamelCa... | 69 |
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_configuration_common import Co... | 69 | 1 |
lowerCamelCase__ = {"""a""": ["""c""", """b"""], """b""": ["""d""", """e"""], """c""": [], """d""": [], """e""": []}
lowerCamelCase__ = ["""a""", """b""", """c""", """d""", """e"""]
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) ... | 69 |
from itertools import permutations
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ ) -> bool:
if num[3] % 2 != 0:
return False
if (num[2] + num[3] + num[4]) % 3 != 0:
return False
if num[5] % 5 != 0:
return False
lowerCAmelCase__ : str = [7, 11, 1... | 69 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase__ = logging.get_logger(__name__)
lowerCamelCase__ = {
"""SCUT-DLVCLab/lilt-roberta-en-base""": (
"""https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/main/config.json"""
... | 69 |
import gc
import unittest
import numpy as np
import torch
from torch.backends.cuda import sdp_kernel
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
from diffusers.utils import randn_tensor, slow, torch_device
from diffusers.utils.testing_uti... | 69 | 1 |
import inspect
import unittest
from transformers import DecisionTransformerConfig, 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_modelin... | 69 |
import numpy as np
import torch
from torch.utils.data import Dataset
from utils import logger
class A__ ( __magic_name__ ):
def __init__( self : int , a : List[str] , a : List[str] ):
'''simple docstring'''
... | 69 | 1 |
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_=28_123 ) -> str:
lowerCAmelCase__ : Tuple = [1] * (limit + 1)
for i in range(2 , int(limit**0.5 ) + 1 ):
sum_divs[i * i] += i
for k in range(i + 1 , limit // i + 1 ):
sum_divs[k * i] += k + i
... | 69 |
from . import __version__
# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
CLOUDFRONT_DISTRIB_PREFIX,
CONFIG_NAME,
DISABLE_TELEMETRY,
DUMMY_INPUTS,
DUMMY_MASK,
ENV_VARS_TRUE_AND_AUTO_VALUES,
ENV_VARS_TRUE_VALUES,
... | 69 | 1 |
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> float:
return round(float(moles / volume ) * nfactor )
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -... | 69 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_remb... | 69 | 1 |
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> float:
lowerCAmelCase__ : Union[str, Any] = (num_of_terms / 2) * (2 * first_term + (num_of_terms - 1) * common_diff)
# formula for sum of series
return total
def ... | 69 |
from typing import Callable, Optional
from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream
class A__ ( __magic_name__ ):
def __init__( self : List[Any] , a : Callable , ... | 69 | 1 |
import math
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> int:
lowerCAmelCase__ : Optional[Any] = len(SCREAMING_SNAKE_CASE_ )
lowerCAmelCase__ : Union[str, Any] = int(math.floor(math.sqrt(SCREAMING_SNAKE_CASE_ ) ) )
lowerCAmel... | 69 |
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
lowerCamelCase__ ... | 69 | 1 |
from math import factorial
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ = 20 ) -> int:
lowerCAmelCase__ : int = 2 * n # middle entry of odd rows starting at row 3 is the solution for n = 1,
# 2, 3,...
lowerCAmelCase__ : Optional[int] = n // 2
return int(fac... | 69 |
import unittest
from transformers import DonutProcessor
lowerCamelCase__ = """naver-clova-ix/donut-base"""
class A__ ( unittest.TestCase ):
def _lowerCamelCase ( self : Dict ):
'''simple docstring'''
... | 69 | 1 |
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ = 1_000_000 ) -> int:
lowerCAmelCase__ : Tuple = 1
lowerCAmelCase__ : int = 1
lowerCAmelCase__ : int = {1: 1}
for inputa in range(2 , SCREAMING_SNAKE_CASE_ ):
lowerCAmelCase__ : Union[s... | 69 |
from numpy import exp, pi, sqrt
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = 0.0 , SCREAMING_SNAKE_CASE_ = 1.0 ) -> int:
return 1 / sqrt(2 * pi * sigma**2 ) * exp(-((x - mu) ** 2) / (2 * sigma**2) )
if __name__ == "__main__":
import doctest
... | 69 | 1 |
from multiprocessing import Lock, Pipe, Process
# lock used to ensure that two processes do not access a pipe at the same time
lowerCamelCase__ = Lock()
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , ... | 69 |
import json
import os
import unittest
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class A__ ( __magic_name__ , unittest.TestCase ):
... | 69 | 1 |
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ ) -> int:
lowerCAmelCase__ : Optional[int] = 1
lowerCAmelCase__ : Union[str, Any] = 2
while i * i <= n:
lowerCAmelCase__ : Optional[Any] = 0
while n % i == 0:
n //= i
multipli... | 69 |
import inspect
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
import torch.utils.checkpoint
from ...models import UNetaDModel, VQModel
from ...schedulers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
Eul... | 69 | 1 |
from typing import Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, v... | 69 |
import os
from collections import deque
import torch
from torch.utils.data import Dataset
class A__ ( __magic_name__ ):
def __init__( self : Union[str, Any] , a : str="" , a : str="train" ):
'''simple docstring'''
... | 69 | 1 |
import json
import os
import re
import sys
import urllib.request
import requests
from bsa import BeautifulSoup
lowerCamelCase__ = {
"""User-Agent""": """Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36"""
""" (KHTML, like Gecko) Chrome/70.0.3538.102 Safari/537.36 Edge/18.19582"... | 69 |
import inspect
import re
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
lowerCamelCase__ = """src/transformers"""
# This is to make sure... | 69 | 1 |
import math
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ ) -> bool:
return math.sqrt(SCREAMING_SNAKE_CASE_ ) * math.sqrt(SCREAMING_SNAKE_CASE_ ) == num
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ ) -> bool:
lowerCAmelCase__ : List[Any] = 0
lowerCAmelCa... | 69 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase__ = {
"""configuration_luke""": ["""LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LukeConfig"""],
"""tokenization_luke""": ["""LukeTokenizer"""],
}
try:
i... | 69 | 1 |
import inspect
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
import torch.utils.checkpoint
from ...models import UNetaDModel, VQModel
from ...schedulers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
Eul... | 69 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowerCamelCase__ = {
"""configuration_chinese_clip""": [
"""CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""ChineseCLIPConfig""",
... | 69 | 1 |
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartaaTokenizer, MBartaaTokenizerFast, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_... | 69 |
import logging
import os
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import List, Optional
import faiss
import torch
from datasets import Features, Sequence, Value, load_dataset
from transformers impo... | 69 | 1 |
from __future__ import annotations
import string
from itertools import cycle, product
from pathlib import Path
lowerCamelCase__ = (
string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
lowerCamelCase__ = [ord(letter) for letter in string.ascii_lowercase]
lowerC... | 69 |
import torch
from diffusers import DDPMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class A__ ( __magic_name__ ):
lowercase = (DDPMParallelScheduler,)
def _lowerCamelCase ( self : str , **a : Optional[i... | 69 | 1 |
import numpy
class A__ :
def __init__( self : Tuple , a : numpy.ndarray , a : numpy.ndarray ):
'''simple docstring'''
lowerCAmelCase__ : int = input_array
# Random initial weights... | 69 |
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class A__ ( __magic_name__ ):
... | 69 | 1 |
from __future__ import annotations
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Tuple:
print(F'''Vertex\tShortest Distance from vertex {src}''' )
for i, d in enumerate(SCREAMING_SNAKE_CASE_ ):
print(F'''{i}\t\t{d}''' )
def lowerCAmelCase__ ... | 69 |
import unittest
from transformers import SqueezeBertConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_ten... | 69 | 1 |
import unittest
from transformers import AutoTokenizer, FalconConfig, 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 impor... | 69 |
lowerCamelCase__ = """Alexander Joslin"""
import operator as op
from .stack import Stack
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ ) -> int:
lowerCAmelCase__ : Union[str, Any] = {'*': op.mul, '/': op.truediv, '+': op.add, '-': op.sub}
lowerCAmelCase__ : Sta... | 69 | 1 |
from __future__ import annotations
from collections.abc import Callable
from typing import Generic, TypeVar
lowerCamelCase__ = TypeVar("""T""")
lowerCamelCase__ = TypeVar("""U""")
class A__ ( Generic[T, U] ):
def __init__( self : Dict , a ... | 69 |
import numpy
class A__ :
def __init__( self : Tuple , a : numpy.ndarray , a : numpy.ndarray ):
'''simple docstring'''
lowerCAmelCase__ : int = input_array
# Random initial weights... | 69 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase__ = {
"""configuration_git""": ["""GIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GitConfig""", """GitVisionConfig"""],
"""processing_git""": ["""GitProcessor"""],
}... | 69 |
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_configuration_common import Co... | 69 | 1 |
import json
import os
import unittest
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class A__ ( __magic_name__ , unittest.TestCase ):
... | 69 |
from itertools import permutations
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ ) -> bool:
if num[3] % 2 != 0:
return False
if (num[2] + num[3] + num[4]) % 3 != 0:
return False
if num[5] % 5 != 0:
return False
lowerCAmelCase__ : str = [7, 11, 1... | 69 | 1 |
import torch
from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class A__ ( __magic_name__ ):
lowercase = (EulerDiscreteScheduler,)
lowercase = 10
d... | 69 |
import gc
import unittest
import numpy as np
import torch
from torch.backends.cuda import sdp_kernel
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
from diffusers.utils import randn_tensor, slow, torch_device
from diffusers.utils.testing_uti... | 69 | 1 |
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> int:
return abs(SCREAMING_SNAKE_CASE_ ) if a == 0 else greatest_common_divisor(b % a , SCREAMING_SNAKE_CASE_ )
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) ... | 69 |
import numpy as np
import torch
from torch.utils.data import Dataset
from utils import logger
class A__ ( __magic_name__ ):
def __init__( self : int , a : List[str] , a : List[str] ):
'''simple docstring'''
... | 69 | 1 |
from typing import TYPE_CHECKING
from ....utils import _LazyModule
lowerCamelCase__ = {"""tokenization_tapex""": ["""TapexTokenizer"""]}
if TYPE_CHECKING:
from .tokenization_tapex import TapexTokenizer
else:
import sys
lowerCamelCase__ = _LazyModule(__name__, global... | 69 |
from . import __version__
# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
CLOUDFRONT_DISTRIB_PREFIX,
CONFIG_NAME,
DISABLE_TELEMETRY,
DUMMY_INPUTS,
DUMMY_MASK,
ENV_VARS_TRUE_AND_AUTO_VALUES,
ENV_VARS_TRUE_VALUES,
... | 69 | 1 |
import math
import torch
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin
class A__ ( __magic_name__ , __magic_n... | 69 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_remb... | 69 | 1 |
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ = 1 , SCREAMING_SNAKE_CASE_ = 1_000 ) -> int:
lowerCAmelCase__ : List[Any] = 1
lowerCAmelCase__ : Optional[Any] = 0
for divide_by_number in range(SCREAMING_SNAKE_CASE_ , digit + 1 ):
lowerCAmelCase_... | 69 |
from typing import Callable, Optional
from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream
class A__ ( __magic_name__ ):
def __init__( self : List[Any] , a : Callable , ... | 69 | 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,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_for... | 69 |
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
lowerCamelCase__ ... | 69 | 1 |
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)
lowerCamelCase__ ... | 69 |
import unittest
from transformers import DonutProcessor
lowerCamelCase__ = """naver-clova-ix/donut-base"""
class A__ ( unittest.TestCase ):
def _lowerCamelCase ( self : Dict ):
'''simple docstring'''
... | 69 | 1 |
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image
from transformers import ... | 69 |
from numpy import exp, pi, sqrt
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = 0.0 , SCREAMING_SNAKE_CASE_ = 1.0 ) -> int:
return 1 / sqrt(2 * pi * sigma**2 ) * exp(-((x - mu) ** 2) / (2 * sigma**2) )
if __name__ == "__main__":
import doctest
... | 69 | 1 |
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data import Data... | 69 |
import json
import os
import unittest
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class A__ ( __magic_name__ , unittest.TestCase ):
... | 69 | 1 |
from __future__ import annotations
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ ) -> list:
if len(SCREAMING_SNAKE_CASE_ ) == 0:
return []
lowerCAmelCase__ , lowerCAmelCase__ : List[str] = min(SCREAMING_SNAKE_CASE_ ), max(SCREAMING_SNAKE_CASE_ )
lowerCAmelCa... | 69 |
import inspect
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
import torch.utils.checkpoint
from ...models import UNetaDModel, VQModel
from ...schedulers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
Eul... | 69 | 1 |
import argparse
from pathlib import Path
import torch
from transformers import OPTConfig, OPTModel
from transformers.utils import logging
logging.set_verbosity_info()
lowerCamelCase__ = logging.get_logger(__name__)
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ ) -> Any:
low... | 69 |
import os
from collections import deque
import torch
from torch.utils.data import Dataset
class A__ ( __magic_name__ ):
def __init__( self : Union[str, Any] , a : str="" , a : str="train" ):
'''simple docstring'''
... | 69 | 1 |
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> list:
lowerCAmelCase__ : Tuple = len(SCREAMING_SNAKE_CASE_ )
lowerCAmelCase__ : Union[str, Any] = []
for i in range(len(SCREAMING_SNAKE_CASE_ ) - pat_len + 1 ):
lowerCAmelCase... | 69 |
import inspect
import re
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
lowerCamelCase__ = """src/transformers"""
# This is to make sure... | 69 | 1 |
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union
import numpy as np
import PIL
from PIL import Image
from ...utils import BaseOutput, is_torch_available, is_transformers_available
@dataclass
class A__ ( __magic_name__ ):
lowerca... | 69 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase__ = {
"""configuration_luke""": ["""LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LukeConfig"""],
"""tokenization_luke""": ["""LukeTokenizer"""],
}
try:
i... | 69 | 1 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.